{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1448,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import os\n",
    "import json\n",
    "import logging\n",
    "import string\n",
    "\n",
    "from scipy.stats import entropy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sqlalchemy import create_engine\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "import networkx as nx\n",
    "\n",
    "if os.getcwd().endswith('notebook'):\n",
    "    os.chdir('..')\n",
    "\n",
    "from rna_learn.alphabet import ALPHABET_DNA, CODON_REDUNDANCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(palette='colorblind', font_scale=1.3)\n",
    "palette = sns.color_palette()\n",
    "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s (%(levelname)s) %(message)s\")\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_path = os.path.join(os.getcwd(), 'data/db/seq.db')\n",
    "engine = create_engine(f'sqlite+pysqlite:///{db_path}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Codon Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "codon_to_aa_table = { \n",
    "    'ATA':'I', 'ATC':'I', 'ATT':'I', 'ATG':'M', \n",
    "    'ACA':'T', 'ACC':'T', 'ACG':'T', 'ACT':'T', \n",
    "    'AAC':'N', 'AAT':'N', 'AAA':'K', 'AAG':'K', \n",
    "    'AGC':'S', 'AGT':'S', 'AGA':'R', 'AGG':'R',                  \n",
    "    'CTA':'L', 'CTC':'L', 'CTG':'L', 'CTT':'L', \n",
    "    'CCA':'P', 'CCC':'P', 'CCG':'P', 'CCT':'P', \n",
    "    'CAC':'H', 'CAT':'H', 'CAA':'Q', 'CAG':'Q', \n",
    "    'CGA':'R', 'CGC':'R', 'CGG':'R', 'CGT':'R', \n",
    "    'GTA':'V', 'GTC':'V', 'GTG':'V', 'GTT':'V', \n",
    "    'GCA':'A', 'GCC':'A', 'GCG':'A', 'GCT':'A', \n",
    "    'GAC':'D', 'GAT':'D', 'GAA':'E', 'GAG':'E', \n",
    "    'GGA':'G', 'GGC':'G', 'GGG':'G', 'GGT':'G', \n",
    "    'TCA':'S', 'TCC':'S', 'TCG':'S', 'TCT':'S', \n",
    "    'TTC':'F', 'TTT':'F', 'TTA':'L', 'TTG':'L', \n",
    "    'TAC':'Y', 'TAT':'Y', 'TAA':'*', 'TAG':'*', \n",
    "    'TGC':'C', 'TGT':'C', 'TGA':'*', 'TGG':'W', \n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "possible_codons = sorted(CODON_REDUNDANCY.keys())\n",
    "len(possible_codons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3282\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>in_test_set</th>\n",
       "      <th>n_total_codons</th>\n",
       "      <th>AAA</th>\n",
       "      <th>AAC</th>\n",
       "      <th>AAG</th>\n",
       "      <th>AAT</th>\n",
       "      <th>ACA</th>\n",
       "      <th>ACC</th>\n",
       "      <th>...</th>\n",
       "      <th>TCG</th>\n",
       "      <th>TCT</th>\n",
       "      <th>TGA</th>\n",
       "      <th>TGC</th>\n",
       "      <th>TGG</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TTA</th>\n",
       "      <th>TTC</th>\n",
       "      <th>TTG</th>\n",
       "      <th>TTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCA_000005825.2</td>\n",
       "      <td>79885</td>\n",
       "      <td>False</td>\n",
       "      <td>1203202</td>\n",
       "      <td>50709</td>\n",
       "      <td>16004</td>\n",
       "      <td>24775</td>\n",
       "      <td>32868</td>\n",
       "      <td>27117</td>\n",
       "      <td>8825</td>\n",
       "      <td>...</td>\n",
       "      <td>6261</td>\n",
       "      <td>17452</td>\n",
       "      <td>677</td>\n",
       "      <td>2894</td>\n",
       "      <td>11946</td>\n",
       "      <td>5312</td>\n",
       "      <td>42464</td>\n",
       "      <td>14432</td>\n",
       "      <td>13363</td>\n",
       "      <td>38272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCA_000005845.2</td>\n",
       "      <td>562</td>\n",
       "      <td>False</td>\n",
       "      <td>1344061</td>\n",
       "      <td>45220</td>\n",
       "      <td>28975</td>\n",
       "      <td>13763</td>\n",
       "      <td>23608</td>\n",
       "      <td>9376</td>\n",
       "      <td>31511</td>\n",
       "      <td>...</td>\n",
       "      <td>11968</td>\n",
       "      <td>11294</td>\n",
       "      <td>1244</td>\n",
       "      <td>8655</td>\n",
       "      <td>20469</td>\n",
       "      <td>6903</td>\n",
       "      <td>18597</td>\n",
       "      <td>22232</td>\n",
       "      <td>18329</td>\n",
       "      <td>29952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCA_000006175.2</td>\n",
       "      <td>2188</td>\n",
       "      <td>True</td>\n",
       "      <td>511762</td>\n",
       "      <td>41277</td>\n",
       "      <td>9506</td>\n",
       "      <td>6208</td>\n",
       "      <td>28398</td>\n",
       "      <td>10533</td>\n",
       "      <td>3688</td>\n",
       "      <td>...</td>\n",
       "      <td>1693</td>\n",
       "      <td>7111</td>\n",
       "      <td>63</td>\n",
       "      <td>1841</td>\n",
       "      <td>2994</td>\n",
       "      <td>4972</td>\n",
       "      <td>27806</td>\n",
       "      <td>4525</td>\n",
       "      <td>7325</td>\n",
       "      <td>14409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCA_000006605.1</td>\n",
       "      <td>38289</td>\n",
       "      <td>False</td>\n",
       "      <td>737577</td>\n",
       "      <td>4666</td>\n",
       "      <td>18501</td>\n",
       "      <td>23297</td>\n",
       "      <td>4442</td>\n",
       "      <td>3555</td>\n",
       "      <td>23745</td>\n",
       "      <td>...</td>\n",
       "      <td>7210</td>\n",
       "      <td>4657</td>\n",
       "      <td>337</td>\n",
       "      <td>4174</td>\n",
       "      <td>10432</td>\n",
       "      <td>1154</td>\n",
       "      <td>1167</td>\n",
       "      <td>18563</td>\n",
       "      <td>7493</td>\n",
       "      <td>4785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCA_000006685.1</td>\n",
       "      <td>83560</td>\n",
       "      <td>False</td>\n",
       "      <td>323926</td>\n",
       "      <td>13771</td>\n",
       "      <td>3527</td>\n",
       "      <td>5541</td>\n",
       "      <td>8332</td>\n",
       "      <td>5378</td>\n",
       "      <td>2884</td>\n",
       "      <td>...</td>\n",
       "      <td>1987</td>\n",
       "      <td>11321</td>\n",
       "      <td>136</td>\n",
       "      <td>1924</td>\n",
       "      <td>3143</td>\n",
       "      <td>3316</td>\n",
       "      <td>11026</td>\n",
       "      <td>5157</td>\n",
       "      <td>6629</td>\n",
       "      <td>10685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession  species_taxid  in_test_set  n_total_codons    AAA  \\\n",
       "0    GCA_000005825.2          79885        False         1203202  50709   \n",
       "1    GCA_000005845.2            562        False         1344061  45220   \n",
       "2    GCA_000006175.2           2188         True          511762  41277   \n",
       "3    GCA_000006605.1          38289        False          737577   4666   \n",
       "4    GCA_000006685.1          83560        False          323926  13771   \n",
       "\n",
       "     AAC    AAG    AAT    ACA    ACC  ...    TCG    TCT   TGA   TGC    TGG  \\\n",
       "0  16004  24775  32868  27117   8825  ...   6261  17452   677  2894  11946   \n",
       "1  28975  13763  23608   9376  31511  ...  11968  11294  1244  8655  20469   \n",
       "2   9506   6208  28398  10533   3688  ...   1693   7111    63  1841   2994   \n",
       "3  18501  23297   4442   3555  23745  ...   7210   4657   337  4174  10432   \n",
       "4   3527   5541   8332   5378   2884  ...   1987  11321   136  1924   3143   \n",
       "\n",
       "    TGT    TTA    TTC    TTG    TTT  \n",
       "0  5312  42464  14432  13363  38272  \n",
       "1  6903  18597  22232  18329  29952  \n",
       "2  4972  27806   4525   7325  14409  \n",
       "3  1154   1167  18563   7493   4785  \n",
       "4  3316  11026   5157   6629  10685  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_frequency_df = pd.read_csv(os.path.join(os.getcwd(), 'data/codon_bias.csv'))\n",
    "print(len(codon_frequency_df))\n",
    "codon_frequency_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2680"
      ]
     },
     "execution_count": 1668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(codon_frequency_df['species_taxid'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3282"
      ]
     },
     "execution_count": 1671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(codon_frequency_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 970,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>growth_tmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCA_000005825.2</td>\n",
       "      <td>79885</td>\n",
       "      <td>31.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GCA_000005845.2</td>\n",
       "      <td>562</td>\n",
       "      <td>37.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCA_000006175.2</td>\n",
       "      <td>2188</td>\n",
       "      <td>36.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCA_000006605.1</td>\n",
       "      <td>38289</td>\n",
       "      <td>37.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCA_000006685.1</td>\n",
       "      <td>83560</td>\n",
       "      <td>37.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession  species_taxid  growth_tmp\n",
       "0    GCA_000005825.2          79885      31.000\n",
       "1    GCA_000005845.2            562      37.875\n",
       "2    GCA_000006175.2           2188      36.600\n",
       "3    GCA_000006605.1          38289      37.000\n",
       "4    GCA_000006685.1          83560      37.000"
      ]
     },
     "execution_count": 970,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature_query = \"\"\"\n",
    "select s.assembly_accession, t.species_taxid, t.growth_tmp from assembly_source as s\n",
    "left join (\n",
    "    select species_taxid, growth_tmp from species_traits\n",
    ") as t\n",
    "on s.species_taxid = t.species_taxid\n",
    "\"\"\"\n",
    "temperatures_df = pd.read_sql(temperature_query, engine)\n",
    "temperatures_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 976,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_temperatures_df = temperatures_df[\n",
    "    ['species_taxid', 'growth_tmp']].groupby('species_taxid').first().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3282"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df = pd.merge(\n",
    "    codon_frequency_df,\n",
    "    temperatures_df,\n",
    "    how='left',\n",
    "    on='assembly_accession'\n",
    ")\n",
    "len(codon_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7111</td>\n",
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       "      <td>4972</td>\n",
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       "      <td>7325</td>\n",
       "      <td>14409</td>\n",
       "      <td>36.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCA_000006605.1</td>\n",
       "      <td>38289</td>\n",
       "      <td>False</td>\n",
       "      <td>737577</td>\n",
       "      <td>4666</td>\n",
       "      <td>18501</td>\n",
       "      <td>23297</td>\n",
       "      <td>4442</td>\n",
       "      <td>3555</td>\n",
       "      <td>23745</td>\n",
       "      <td>...</td>\n",
       "      <td>4657</td>\n",
       "      <td>337</td>\n",
       "      <td>4174</td>\n",
       "      <td>10432</td>\n",
       "      <td>1154</td>\n",
       "      <td>1167</td>\n",
       "      <td>18563</td>\n",
       "      <td>7493</td>\n",
       "      <td>4785</td>\n",
       "      <td>37.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCA_000006685.1</td>\n",
       "      <td>83560</td>\n",
       "      <td>False</td>\n",
       "      <td>323926</td>\n",
       "      <td>13771</td>\n",
       "      <td>3527</td>\n",
       "      <td>5541</td>\n",
       "      <td>8332</td>\n",
       "      <td>5378</td>\n",
       "      <td>2884</td>\n",
       "      <td>...</td>\n",
       "      <td>11321</td>\n",
       "      <td>136</td>\n",
       "      <td>1924</td>\n",
       "      <td>3143</td>\n",
       "      <td>3316</td>\n",
       "      <td>11026</td>\n",
       "      <td>5157</td>\n",
       "      <td>6629</td>\n",
       "      <td>10685</td>\n",
       "      <td>37.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession  species_taxid  in_test_set  n_total_codons    AAA  \\\n",
       "0    GCA_000005825.2          79885        False         1203202  50709   \n",
       "1    GCA_000005845.2            562        False         1344061  45220   \n",
       "2    GCA_000006175.2           2188         True          511762  41277   \n",
       "3    GCA_000006605.1          38289        False          737577   4666   \n",
       "4    GCA_000006685.1          83560        False          323926  13771   \n",
       "\n",
       "     AAC    AAG    AAT    ACA    ACC  ...    TCT   TGA   TGC    TGG   TGT  \\\n",
       "0  16004  24775  32868  27117   8825  ...  17452   677  2894  11946  5312   \n",
       "1  28975  13763  23608   9376  31511  ...  11294  1244  8655  20469  6903   \n",
       "2   9506   6208  28398  10533   3688  ...   7111    63  1841   2994  4972   \n",
       "3  18501  23297   4442   3555  23745  ...   4657   337  4174  10432  1154   \n",
       "4   3527   5541   8332   5378   2884  ...  11321   136  1924   3143  3316   \n",
       "\n",
       "     TTA    TTC    TTG    TTT  growth_tmp  \n",
       "0  42464  14432  13363  38272      31.000  \n",
       "1  18597  22232  18329  29952      37.875  \n",
       "2  27806   4525   7325  14409      36.600  \n",
       "3   1167  18563   7493   4785      37.000  \n",
       "4  11026   5157   6629  10685      37.000  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Codon ratio model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_codon_ratio(codon_df):\n",
    "    processed_codons = set()\n",
    "    for codon in sorted(CODON_REDUNDANCY.keys()):\n",
    "        if codon in processed_codons:\n",
    "            continue\n",
    "        elif len(CODON_REDUNDANCY[codon]) <= 1:\n",
    "            processed_codons.add(codon)\n",
    "            continue\n",
    "        \n",
    "        synonymous_codons = CODON_REDUNDANCY[codon]\n",
    "        for c, _ in synonymous_codons:\n",
    "            processed_codons.add(c)\n",
    "            codon_df[f'{c}_ratio'] = codon_df[c] / sum((codon_df[cod] for cod, _ in synonymous_codons))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_codon_ratio(codon_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2222 1060\n"
     ]
    }
   ],
   "source": [
    "ratio_columns = [c for c in codon_df.columns if c.endswith('_ratio')]\n",
    "\n",
    "x_train = codon_df[~codon_df['in_test_set']][ratio_columns].values\n",
    "y_train = codon_df[~codon_df['in_test_set']]['growth_tmp'].values\n",
    "\n",
    "x_test = codon_df[codon_df['in_test_set']][ratio_columns].values\n",
    "y_test = codon_df[codon_df['in_test_set']]['growth_tmp'].values\n",
    "\n",
    "print(len(x_train), len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_linear_regression(y_actual, x):\n",
    "    X = sm.add_constant(x)\n",
    "    model = sm.OLS(y_actual, X)\n",
    "    results = model.fit()\n",
    "    y_fit = results.predict(X)\n",
    "    return results, y_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "results, y_fit_train = fit_linear_regression(y_train, x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1664,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_true_vs_prediction_per_specie(y_true, y_pred, ax=None):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    \n",
    "    palette = sns.color_palette()\n",
    "    \n",
    "    x_straight = range(int(np.min(y_true)) - 1, int(np.max(y_true)) + 1)\n",
    "    ax.plot(x_straight, x_straight, '--', color=palette[1], linewidth=2)\n",
    "    \n",
    "    ax.scatter(y_true, y_pred, color=palette[0], alpha=0.5, label='Species')\n",
    "    \n",
    "    ax.set_xlabel('Temperature °C (truth)')\n",
    "    ax.set_ylabel('Temperature °C (prediction)')\n",
    "    return ax\n",
    "\n",
    "\n",
    "def compute_and_plot_pattern_against_ogt(y_actual, x, results, ax=None):\n",
    "    palette = sns.color_palette()\n",
    "\n",
    "    X = sm.add_constant(x)\n",
    "    y_fit = results.predict(X)\n",
    "    \n",
    "    std, upper, lower = wls_prediction_std(results)\n",
    "    \n",
    "    ax = plot_true_vs_prediction_per_specie(y_actual, y_fit, ax=ax)\n",
    "    \n",
    "    df_data = [\n",
    "        [y_actual[i], y_fit[i], std[i]]\n",
    "        for i in range(len(y_actual))\n",
    "    ]\n",
    "    df = pd.DataFrame(df_data, columns=['growth_tmp_actual', 'growth_tmp_prediction', 'growth_tmp_std'])\n",
    "    \n",
    "    pred_mean = df[['growth_tmp_actual', 'growth_tmp_prediction']].groupby('growth_tmp_actual').mean()\n",
    "    std_mean = df[['growth_tmp_actual', 'growth_tmp_std']].groupby('growth_tmp_actual').mean().reset_index()\n",
    "    x_fill = std_mean['growth_tmp_actual'].values\n",
    "    x_base = pred_mean['growth_tmp_prediction'].values\n",
    "    y_fill_1 = x_base - 2 * std_mean['growth_tmp_std'].values\n",
    "    y_fill_2 = x_base + 2 * std_mean['growth_tmp_std'].values\n",
    "    ax.fill_between(x_fill, y_fill_1, y_fill_2, alpha=0.2, color=palette[1], label='Uncertainty');\n",
    "\n",
    "    ax.legend(loc='upper left')\n",
    "    \n",
    "    return ax, y_fit\n",
    "\n",
    "\n",
    "def compute_mae(y_true, y_pred):\n",
    "    return np.mean(np.abs(y_true - y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, y_fit_test = compute_and_plot_pattern_against_ogt(y_test, x_test, results)\n",
    "ax.set_title((\n",
    "    f'Growth temperature prediction from codon bias\\n\\n'\n",
    "    f'R-squared = {results.rsquared:.2f} - Mean absolute error = {compute_mae(y_test, y_fit_test):.2f} °C\\n'\n",
    "));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1658,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mae(y_true, y_pred):\n",
    "    return np.mean(np.abs(y_true - y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.712866857458104"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae(y_train, y_fit_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.045457980527855"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae(y_test, y_fit_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phylogeny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>Azorhizobium caulinodans</td>\n",
       "      <td>Azorhizobium</td>\n",
       "      <td>Xanthobacteraceae</td>\n",
       "      <td>Rhizobiales</td>\n",
       "      <td>Alphaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Cellulomonas gilvus</td>\n",
       "      <td>Cellulomonas</td>\n",
       "      <td>Cellulomonadaceae</td>\n",
       "      <td>Micrococcales</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>Dictyoglomus thermophilum</td>\n",
       "      <td>Dictyoglomus</td>\n",
       "      <td>Dictyoglomaceae</td>\n",
       "      <td>Dictyoglomales</td>\n",
       "      <td>Dictyoglomia</td>\n",
       "      <td>Dictyoglomi</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>Pelobacter carbinolicus</td>\n",
       "      <td>Pelobacter</td>\n",
       "      <td>Desulfuromonadaceae</td>\n",
       "      <td>Desulfuromonadales</td>\n",
       "      <td>Deltaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>Shewanella putrefaciens</td>\n",
       "      <td>Shewanella</td>\n",
       "      <td>Shewanellaceae</td>\n",
       "      <td>Alteromonadales</td>\n",
       "      <td>Gammaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   species_taxid                    species         genus  \\\n",
       "0              7   Azorhizobium caulinodans  Azorhizobium   \n",
       "1             11        Cellulomonas gilvus  Cellulomonas   \n",
       "2             14  Dictyoglomus thermophilum  Dictyoglomus   \n",
       "3             19    Pelobacter carbinolicus    Pelobacter   \n",
       "4             24    Shewanella putrefaciens    Shewanella   \n",
       "\n",
       "                family               order                class  \\\n",
       "0    Xanthobacteraceae         Rhizobiales  Alphaproteobacteria   \n",
       "1    Cellulomonadaceae       Micrococcales       Actinobacteria   \n",
       "2      Dictyoglomaceae      Dictyoglomales         Dictyoglomia   \n",
       "3  Desulfuromonadaceae  Desulfuromonadales  Deltaproteobacteria   \n",
       "4       Shewanellaceae     Alteromonadales  Gammaproteobacteria   \n",
       "\n",
       "           phylum superkingdom  \n",
       "0  Proteobacteria     Bacteria  \n",
       "1  Actinobacteria     Bacteria  \n",
       "2     Dictyoglomi     Bacteria  \n",
       "3  Proteobacteria     Bacteria  \n",
       "4  Proteobacteria     Bacteria  "
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phylogeny_query = \"\"\"\n",
    "select species_taxid, species, genus, family, \"order\", class, phylum, superkingdom\n",
    "from species_traits\n",
    "where species_taxid in (\n",
    "    select species_taxid from train_test_split\n",
    "    where in_test_set = 1\n",
    ")\n",
    "\"\"\"\n",
    "phylogeny_df = pd.read_sql(phylogeny_query, engine)\n",
    "phylogeny_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superkingdom</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Archaea</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bacteria</th>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              species_taxid\n",
       "superkingdom               \n",
       "Archaea                  45\n",
       "Bacteria                525"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phylogeny_df[['superkingdom', 'species_taxid']].groupby('superkingdom').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superkingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Archaea</th>\n",
       "      <th>Crenarchaeota</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euryarchaeota</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">Bacteria</th>\n",
       "      <th>Acidobacteria</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actinobacteria</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aquificae</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bacteroidetes</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calditrichaeota</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chlamydiae</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chlorobi</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chloroflexi</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyanobacteria</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deinococcus-Thermus</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dictyoglomi</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Firmicutes</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusobacteria</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gemmatimonadetes</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrospirae</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Planctomycetes</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proteobacteria</th>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spirochaetes</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Synergistetes</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenericutes</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thermotogae</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  species_taxid\n",
       "superkingdom phylum                            \n",
       "Archaea      Crenarchaeota                   13\n",
       "             Euryarchaeota                   32\n",
       "Bacteria     Acidobacteria                    1\n",
       "             Actinobacteria                  77\n",
       "             Aquificae                        4\n",
       "             Bacteroidetes                   26\n",
       "             Calditrichaeota                  1\n",
       "             Chlamydiae                       2\n",
       "             Chlorobi                         1\n",
       "             Chloroflexi                      5\n",
       "             Cyanobacteria                    2\n",
       "             Deinococcus-Thermus              7\n",
       "             Dictyoglomi                      1\n",
       "             Firmicutes                     146\n",
       "             Fusobacteria                     1\n",
       "             Gemmatimonadetes                 1\n",
       "             Nitrospirae                      1\n",
       "             Planctomycetes                   1\n",
       "             Proteobacteria                 215\n",
       "             Spirochaetes                     4\n",
       "             Synergistetes                    2\n",
       "             Tenericutes                     18\n",
       "             Thermotogae                      9"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phylogeny_df[['superkingdom', 'phylum', 'species_taxid']].groupby(['superkingdom', 'phylum']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superkingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Archaea</th>\n",
       "      <th>Crenarchaeota</th>\n",
       "      <th>Thermoprotei</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Euryarchaeota</th>\n",
       "      <th>Archaeoglobi</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Halobacteria</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methanococci</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methanomicrobia</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methanopyri</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thermococci</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thermoplasmata</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"36\" valign=\"top\">Bacteria</th>\n",
       "      <th>Acidobacteria</th>\n",
       "      <th>Acidobacteriia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Actinobacteria</th>\n",
       "      <th>Actinobacteria</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubrobacteria</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aquificae</th>\n",
       "      <th>Aquificae</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Bacteroidetes</th>\n",
       "      <th>Bacteroidia</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chitinophagia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cytophagia</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flavobacteriia</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sphingobacteriia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calditrichaeota</th>\n",
       "      <th>Calditrichae</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chlamydiae</th>\n",
       "      <th>Chlamydiia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chlorobi</th>\n",
       "      <th>Chlorobia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Chloroflexi</th>\n",
       "      <th>Anaerolineae</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chloroflexia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thermomicrobia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deinococcus-Thermus</th>\n",
       "      <th>Deinococci</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dictyoglomi</th>\n",
       "      <th>Dictyoglomia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Firmicutes</th>\n",
       "      <th>Bacilli</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clostridia</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Negativicutes</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tissierellia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusobacteria</th>\n",
       "      <th>Fusobacteriia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gemmatimonadetes</th>\n",
       "      <th>Gemmatimonadetes</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitrospirae</th>\n",
       "      <th>Nitrospira</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Planctomycetes</th>\n",
       "      <th>Planctomycetia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Proteobacteria</th>\n",
       "      <th>Acidithiobacillia</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alphaproteobacteria</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betaproteobacteria</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deltaproteobacteria</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilonproteobacteria</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gammaproteobacteria</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oligoflexia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spirochaetes</th>\n",
       "      <th>Spirochaetia</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Synergistetes</th>\n",
       "      <th>Synergistia</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenericutes</th>\n",
       "      <th>Mollicutes</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thermotogae</th>\n",
       "      <th>Thermotogae</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        species_taxid\n",
       "superkingdom phylum              class                               \n",
       "Archaea      Crenarchaeota       Thermoprotei                      13\n",
       "             Euryarchaeota       Archaeoglobi                       2\n",
       "                                 Halobacteria                      11\n",
       "                                 Methanococci                       2\n",
       "                                 Methanomicrobia                    5\n",
       "                                 Methanopyri                        1\n",
       "                                 Thermococci                       10\n",
       "                                 Thermoplasmata                     1\n",
       "Bacteria     Acidobacteria       Acidobacteriia                     1\n",
       "             Actinobacteria      Actinobacteria                    75\n",
       "                                 Rubrobacteria                      2\n",
       "             Aquificae           Aquificae                          4\n",
       "             Bacteroidetes       Bacteroidia                        7\n",
       "                                 Chitinophagia                      2\n",
       "                                 Cytophagia                         3\n",
       "                                 Flavobacteriia                    12\n",
       "                                 Sphingobacteriia                   1\n",
       "             Calditrichaeota     Calditrichae                       1\n",
       "             Chlamydiae          Chlamydiia                         2\n",
       "             Chlorobi            Chlorobia                          1\n",
       "             Chloroflexi         Anaerolineae                       3\n",
       "                                 Chloroflexia                       1\n",
       "                                 Thermomicrobia                     1\n",
       "             Deinococcus-Thermus Deinococci                         7\n",
       "             Dictyoglomi         Dictyoglomia                       1\n",
       "             Firmicutes          Bacilli                           94\n",
       "                                 Clostridia                        47\n",
       "                                 Negativicutes                      3\n",
       "                                 Tissierellia                       2\n",
       "             Fusobacteria        Fusobacteriia                      1\n",
       "             Gemmatimonadetes    Gemmatimonadetes                   1\n",
       "             Nitrospirae         Nitrospira                         1\n",
       "             Planctomycetes      Planctomycetia                     1\n",
       "             Proteobacteria      Acidithiobacillia                  1\n",
       "                                 Alphaproteobacteria               46\n",
       "                                 Betaproteobacteria                36\n",
       "                                 Deltaproteobacteria               20\n",
       "                                 Epsilonproteobacteria             12\n",
       "                                 Gammaproteobacteria               98\n",
       "                                 Oligoflexia                        2\n",
       "             Spirochaetes        Spirochaetia                       4\n",
       "             Synergistetes       Synergistia                        2\n",
       "             Tenericutes         Mollicutes                        18\n",
       "             Thermotogae         Thermotogae                        9"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phylogeny_df[[\n",
    "    'superkingdom', \n",
    "    'phylum', \n",
    "    'class', \n",
    "    'species_taxid',\n",
    "]].groupby([\n",
    "    'superkingdom', \n",
    "    'phylum', \n",
    "    'class',\n",
    "]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3282\n"
     ]
    }
   ],
   "source": [
    "codon_with_phylogeny_df = pd.merge(\n",
    "    codon_df,\n",
    "    phylogeny_df,\n",
    "    how='left',\n",
    "    on='species_taxid',\n",
    ")\n",
    "print(len(codon_with_phylogeny_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "codon_test = codon_with_phylogeny_df[codon_with_phylogeny_df['in_test_set']].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_true_vs_prediction_per_segment(df, y_true, y_pred, segmentation_column, n_items_threshold=3):\n",
    "    threshold = 5\n",
    "    possible_values = [\n",
    "        v for v in df[segmentation_column].unique()\n",
    "        if len(df[df[segmentation_column] == v].index) > n_items_threshold\n",
    "    ]\n",
    "    n_cols = 2\n",
    "    n_rows = int(np.ceil(len(possible_values) / n_cols))\n",
    "    _, axes = plt.subplots(n_rows, 2, figsize=(8 * n_cols, 8 * n_rows))\n",
    "    axes = axes.flatten()\n",
    "    \n",
    "    palette = sns.color_palette()\n",
    "    \n",
    "    x_straight = range(int(np.min(y_true)) - 1, int(np.max(y_true)) + 1)\n",
    "    for i in range(len(axes)):\n",
    "        ax = axes[i]\n",
    "        \n",
    "        if (i + 1) > len(possible_values):\n",
    "            ax.remove()\n",
    "            continue\n",
    "        \n",
    "        possible_value = possible_values[i]\n",
    "        \n",
    "        ix = df[df[segmentation_column] == possible_value].index\n",
    "        if len(ix) > n_items_threshold:\n",
    "            ax.plot(x_straight, x_straight, '--', color=palette[1], linewidth=2)\n",
    "            ax.scatter(y_true[ix], y_pred[ix], alpha=0.5, label=possible_value)\n",
    "            ax.set_title(possible_value)\n",
    "\n",
    "            ax.set_xlabel('Temperature °C (truth)')\n",
    "            ax.set_ylabel('Temperature °C (prediction)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_true_vs_prediction_per_segment(\n",
    "    codon_test, \n",
    "    y_test, \n",
    "    y_fit_test,\n",
    "    segmentation_column='superkingdom',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x3456 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_true_vs_prediction_per_segment(\n",
    "    codon_test, \n",
    "    y_test, \n",
    "    y_fit_test,\n",
    "    segmentation_column='phylum',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.458943  ,   3.56613788,   1.89280513,  -7.77649539,\n",
       "        13.23543839,  -4.48495806,  -8.33884914,   4.19664132,\n",
       "        14.08610888, -16.34178529,  19.64366688, -52.4344612 ,\n",
       "         9.50061777,  -7.53049763,  52.62140248,   5.02500039,\n",
       "        11.96172608, -25.60271295,  16.62765253, -52.09826697,\n",
       "        49.54554392,  10.38126011, -25.16539965,  20.24308254,\n",
       "         5.36971817,   0.08922483,   8.26007106,  -2.80112805,\n",
       "       -27.13179652,  -5.08726006,  19.44018735,  18.23781223,\n",
       "        14.8761475 ,  -6.28605849, -20.38055256, -17.2483225 ,\n",
       "        17.89322537,  16.60450367,  -0.35260828,   5.81155129,\n",
       "         3.27831769,   2.18062532,  36.36793316,   3.72197605,\n",
       "        -5.86752807, -28.76343814,   8.63712107, -29.17868158,\n",
       "         5.43905798,  20.56144553,  -8.70042447,   3.93264387,\n",
       "       -11.37909354,  21.60581715,  -8.55754133,   4.7555653 ,\n",
       "         9.26091904,  -3.05072275,   8.50966575, -12.96993379,\n",
       "        18.42887679,   6.84304768,  -1.38410467])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = np.abs(results.params[1:] / results.bse[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.10214631,  1.12424697,  2.6831855 ,  4.59094671,  0.88468653,\n",
       "        2.14286898,  1.02893047,  3.40800832,  3.74120518,  5.05321843,\n",
       "        8.21709624,  2.41649738,  2.5678636 , 14.77510659,  0.81312307,\n",
       "        2.47547639,  4.04069409,  3.02230881,  6.70428408,  8.89263781,\n",
       "        4.01927352,  9.34370562,  7.86198486,  2.9963364 ,  0.04965069,\n",
       "        3.57916173,  1.21616973,  5.6501089 ,  1.25242067,  5.71847886,\n",
       "        5.93631885,  3.15133425,  1.42807991,  2.60706082,  4.58418159,\n",
       "        4.92803329,  4.40031427,  0.22455535,  3.72856248,  1.3218534 ,\n",
       "        0.86651521,  6.54727092,  0.78451273,  1.26013672,  6.06032928,\n",
       "        1.96159608,  7.03308547,  1.5169691 ,  4.33069267,  2.16770285,\n",
       "        1.03253919,  2.67752474,  6.75281935,  3.43189676,  1.93476195,\n",
       "        6.37077613,  1.31184561,  3.64011414,  6.06731715,  8.7139013 ,\n",
       "        3.74224568,  0.76020899])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.01927352, 9.34370562, 7.86198486])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance[20:23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ATT_ratio', 'ATC_ratio', 'ATA_ratio']"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_columns[20:23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = [\n",
    "    'AGA_ratio', \n",
    "    'AGG_ratio', \n",
    "    'CGT_ratio', \n",
    "    'CGC_ratio', \n",
    "    'CGA_ratio', \n",
    "    'CGG_ratio',\n",
    "    'ATT_ratio', \n",
    "    'ATC_ratio', \n",
    "    'ATA_ratio',\n",
    "]\n",
    "res, f = fit_linear_regression(\n",
    "    y_actual=y_train,\n",
    "    x=codon_df[~codon_df['in_test_set']][cols].values\n",
    ")\n",
    "compute_and_plot_pattern_against_ogt(\n",
    "    y_test, \n",
    "    codon_df[codon_df['in_test_set']][cols].values, \n",
    "    res\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "def amino_acid_importance(y, x, results, possible_codons, ratio_columns):\n",
    "    aa_importance = {}\n",
    "    \n",
    "    weights_importance = np.abs(results.params[1:] / results.bse[1:])\n",
    "    weights_importance_dict = {\n",
    "        ratio_columns[i].replace('_ratio', ''): weights_importance[i]\n",
    "        for i in range(len(weights_importance))\n",
    "    }\n",
    "    \n",
    "    processed_codons = set()\n",
    "    for codon in possible_codons:\n",
    "        if len(CODON_REDUNDANCY[codon]) <= 1:\n",
    "            continue\n",
    "        elif codon in processed_codons:\n",
    "            continue\n",
    "            \n",
    "        importance_factors = []\n",
    "        for c, _ in CODON_REDUNDANCY[codon]:\n",
    "            if c in processed_codons:\n",
    "                continue\n",
    "                \n",
    "            processed_codons.add(c)\n",
    "            \n",
    "            importance_factors.append(\n",
    "                weights_importance_dict[c]\n",
    "            )\n",
    "            \n",
    "        aa = codon_to_aa_table[codon]\n",
    "            \n",
    "        aa_importance[aa] = np.mean(importance_factors)\n",
    "    \n",
    "    return aa_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa_importance = amino_acid_importance(y_test, x_test, results, possible_codons, ratio_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('C', 7.390609225741814),\n",
       " ('I', 7.074987997573898),\n",
       " ('R', 6.128497902148081),\n",
       " ('P', 4.639331818976233),\n",
       " ('S', 4.324754041065607),\n",
       " ('*', 3.9124782796655353),\n",
       " ('G', 3.7105858281695188),\n",
       " ('A', 3.6630624105820284),\n",
       " ('N', 3.637066104298797),\n",
       " ('L', 3.5165006870361193),\n",
       " ('V', 3.1576465337559703),\n",
       " ('Y', 2.4759798750799895),\n",
       " ('H', 2.3976657325528703),\n",
       " ('F', 2.2512273396487776),\n",
       " ('E', 1.976558913808437),\n",
       " ('T', 1.8661235754655932),\n",
       " ('K', 1.6131966375738807),\n",
       " ('Q', 1.5229935480855108),\n",
       " ('D', 1.0941843033942251)]"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(k, v) for k, v in aa_importance.items()], key=lambda t: t[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>organism_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCA_000970025.1</td>\n",
       "      <td>2208</td>\n",
       "      <td>Methanosarcina barkeri MS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession  species_taxid              organism_name\n",
       "0    GCA_000970025.1           2208  Methanosarcina barkeri MS"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = \"\"\"\n",
    "select assembly_accession, species_taxid, organism_name\n",
    "from assembly_source \n",
    "where organism_name like '%Methanosarcina barkeri%'\n",
    "\"\"\"\n",
    "pd.read_sql(q, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TAG_ratio</th>\n",
       "      <th>growth_tmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>0.045273</td>\n",
       "      <td>37.633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TAG_ratio  growth_tmp\n",
       "1324   0.045273      37.633"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df[codon_df['assembly_accession'] == 'GCA_000970025.1'][['TAG_ratio', 'growth_tmp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TAG_ratio     0.181084\n",
       "growth_tmp          31\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df.iloc[0][['TAG_ratio', 'growth_tmp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>TAG_ratio</th>\n",
       "      <th>TAA_ratio</th>\n",
       "      <th>TGA_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>GCA_001889405.1</td>\n",
       "      <td>2177</td>\n",
       "      <td>30.000</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.489415</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>GCA_000196655.1</td>\n",
       "      <td>2322</td>\n",
       "      <td>50.000</td>\n",
       "      <td>0.010686</td>\n",
       "      <td>0.677649</td>\n",
       "      <td>0.311665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>GCA_000970325.1</td>\n",
       "      <td>2226</td>\n",
       "      <td>31.400</td>\n",
       "      <td>0.011374</td>\n",
       "      <td>0.433576</td>\n",
       "      <td>0.555050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>GCA_000025865.1</td>\n",
       "      <td>2176</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.011575</td>\n",
       "      <td>0.478108</td>\n",
       "      <td>0.510317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>GCA_000013725.1</td>\n",
       "      <td>29291</td>\n",
       "      <td>22.167</td>\n",
       "      <td>0.020696</td>\n",
       "      <td>0.451783</td>\n",
       "      <td>0.527521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>GCA_000019905.1</td>\n",
       "      <td>332410</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.033167</td>\n",
       "      <td>0.584743</td>\n",
       "      <td>0.382090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>GCA_000970265.1</td>\n",
       "      <td>170861</td>\n",
       "      <td>24.100</td>\n",
       "      <td>0.035148</td>\n",
       "      <td>0.502333</td>\n",
       "      <td>0.462519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>GCA_000969925.1</td>\n",
       "      <td>2210</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0.037416</td>\n",
       "      <td>0.545490</td>\n",
       "      <td>0.417093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>GCA_000023205.1</td>\n",
       "      <td>1270</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.037567</td>\n",
       "      <td>0.019678</td>\n",
       "      <td>0.942755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>GCA_000969885.1</td>\n",
       "      <td>2210</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0.037588</td>\n",
       "      <td>0.553641</td>\n",
       "      <td>0.408771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>GCA_000367205.1</td>\n",
       "      <td>33010</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.037913</td>\n",
       "      <td>0.039251</td>\n",
       "      <td>0.922837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>GCA_001304615.2</td>\n",
       "      <td>1715806</td>\n",
       "      <td>45.000</td>\n",
       "      <td>0.038619</td>\n",
       "      <td>0.544374</td>\n",
       "      <td>0.417007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>GCA_002158865.1</td>\n",
       "      <td>1082851</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.038783</td>\n",
       "      <td>0.058174</td>\n",
       "      <td>0.903043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>GCA_001275345.1</td>\n",
       "      <td>71999</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.039391</td>\n",
       "      <td>0.016115</td>\n",
       "      <td>0.944494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>GCA_000299435.1</td>\n",
       "      <td>132920</td>\n",
       "      <td>25.000</td>\n",
       "      <td>0.039683</td>\n",
       "      <td>0.559163</td>\n",
       "      <td>0.401154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>GCA_000970205.1</td>\n",
       "      <td>2209</td>\n",
       "      <td>36.500</td>\n",
       "      <td>0.039820</td>\n",
       "      <td>0.526647</td>\n",
       "      <td>0.433533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>GCA_000970285.1</td>\n",
       "      <td>418008</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.039863</td>\n",
       "      <td>0.527757</td>\n",
       "      <td>0.432380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>GCA_004363315.1</td>\n",
       "      <td>360241</td>\n",
       "      <td>45.000</td>\n",
       "      <td>0.040320</td>\n",
       "      <td>0.101560</td>\n",
       "      <td>0.858121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>GCA_008329925.1</td>\n",
       "      <td>34103</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0.042056</td>\n",
       "      <td>0.034787</td>\n",
       "      <td>0.923157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3167</th>\n",
       "      <td>GCA_900476125.1</td>\n",
       "      <td>45363</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.042748</td>\n",
       "      <td>0.238931</td>\n",
       "      <td>0.718321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>GCA_000023405.1</td>\n",
       "      <td>43669</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.044980</td>\n",
       "      <td>0.008801</td>\n",
       "      <td>0.946219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>GCA_003355475.1</td>\n",
       "      <td>556288</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.045198</td>\n",
       "      <td>0.009102</td>\n",
       "      <td>0.945700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>GCA_000970025.1</td>\n",
       "      <td>2208</td>\n",
       "      <td>37.633</td>\n",
       "      <td>0.045273</td>\n",
       "      <td>0.580011</td>\n",
       "      <td>0.374715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>GCA_900129205.1</td>\n",
       "      <td>213588</td>\n",
       "      <td>50.550</td>\n",
       "      <td>0.045616</td>\n",
       "      <td>0.151904</td>\n",
       "      <td>0.802480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>GCA_000970085.1</td>\n",
       "      <td>38027</td>\n",
       "      <td>36.050</td>\n",
       "      <td>0.046418</td>\n",
       "      <td>0.495964</td>\n",
       "      <td>0.457619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>GCA_000019785.1</td>\n",
       "      <td>34029</td>\n",
       "      <td>23.000</td>\n",
       "      <td>0.046757</td>\n",
       "      <td>0.049049</td>\n",
       "      <td>0.904194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>GCA_000186385.1</td>\n",
       "      <td>309887</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.241115</td>\n",
       "      <td>0.712010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3109</th>\n",
       "      <td>GCA_900186975.1</td>\n",
       "      <td>33011</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.047645</td>\n",
       "      <td>0.023823</td>\n",
       "      <td>0.928532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>GCA_000725405.1</td>\n",
       "      <td>1274</td>\n",
       "      <td>31.500</td>\n",
       "      <td>0.048267</td>\n",
       "      <td>0.024572</td>\n",
       "      <td>0.927161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2738</th>\n",
       "      <td>GCA_008124525.1</td>\n",
       "      <td>1189325</td>\n",
       "      <td>50.000</td>\n",
       "      <td>0.048680</td>\n",
       "      <td>0.035310</td>\n",
       "      <td>0.916010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     assembly_accession  species_taxid  growth_tmp  TAG_ratio  TAA_ratio  \\\n",
       "1795    GCA_001889405.1           2177      30.000   0.010585   0.489415   \n",
       "705     GCA_000196655.1           2322      50.000   0.010686   0.677649   \n",
       "1329    GCA_000970325.1           2226      31.400   0.011374   0.433576   \n",
       "438     GCA_000025865.1           2176      37.000   0.011575   0.478108   \n",
       "170     GCA_000013725.1          29291      22.167   0.020696   0.451783   \n",
       "298     GCA_000019905.1         332410      29.000   0.033167   0.584743   \n",
       "1327    GCA_000970265.1         170861      24.100   0.035148   0.502333   \n",
       "1323    GCA_000969925.1           2210      51.000   0.037416   0.545490   \n",
       "357     GCA_000023205.1           1270      29.000   0.037567   0.019678   \n",
       "1321    GCA_000969885.1           2210      51.000   0.037588   0.553641   \n",
       "1019    GCA_000367205.1          33010      37.000   0.037913   0.039251   \n",
       "1466    GCA_001304615.2        1715806      45.000   0.038619   0.544374   \n",
       "1916    GCA_002158865.1        1082851      28.000   0.038783   0.058174   \n",
       "1444    GCA_001275345.1          71999      29.000   0.039391   0.016115   \n",
       "932     GCA_000299435.1         132920      25.000   0.039683   0.559163   \n",
       "1326    GCA_000970205.1           2209      36.500   0.039820   0.526647   \n",
       "1328    GCA_000970285.1         418008      37.000   0.039863   0.527757   \n",
       "2556    GCA_004363315.1         360241      45.000   0.040320   0.101560   \n",
       "2755    GCA_008329925.1          34103      26.000   0.042056   0.034787   \n",
       "3167    GCA_900476125.1          45363      37.000   0.042748   0.238931   \n",
       "363     GCA_000023405.1          43669      29.000   0.044980   0.008801   \n",
       "2284    GCA_003355475.1         556288      28.000   0.045198   0.009102   \n",
       "1324    GCA_000970025.1           2208      37.633   0.045273   0.580011   \n",
       "3016    GCA_900129205.1         213588      50.550   0.045616   0.151904   \n",
       "1325    GCA_000970085.1          38027      36.050   0.046418   0.495964   \n",
       "295     GCA_000019785.1          34029      23.000   0.046757   0.049049   \n",
       "651     GCA_000186385.1         309887      28.000   0.046875   0.241115   \n",
       "3109    GCA_900186975.1          33011      37.000   0.047645   0.023823   \n",
       "1154    GCA_000725405.1           1274      31.500   0.048267   0.024572   \n",
       "2738    GCA_008124525.1        1189325      50.000   0.048680   0.035310   \n",
       "\n",
       "      TGA_ratio  \n",
       "1795   0.500000  \n",
       "705    0.311665  \n",
       "1329   0.555050  \n",
       "438    0.510317  \n",
       "170    0.527521  \n",
       "298    0.382090  \n",
       "1327   0.462519  \n",
       "1323   0.417093  \n",
       "357    0.942755  \n",
       "1321   0.408771  \n",
       "1019   0.922837  \n",
       "1466   0.417007  \n",
       "1916   0.903043  \n",
       "1444   0.944494  \n",
       "932    0.401154  \n",
       "1326   0.433533  \n",
       "1328   0.432380  \n",
       "2556   0.858121  \n",
       "2755   0.923157  \n",
       "3167   0.718321  \n",
       "363    0.946219  \n",
       "2284   0.945700  \n",
       "1324   0.374715  \n",
       "3016   0.802480  \n",
       "1325   0.457619  \n",
       "295    0.904194  \n",
       "651    0.712010  \n",
       "3109   0.928532  \n",
       "1154   0.927161  \n",
       "2738   0.916010  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df.sort_values(['TAG_ratio']).head(30)[[\n",
    "    'assembly_accession', \n",
    "    'species_taxid', \n",
    "    'growth_tmp', \n",
    "    'TAG_ratio',\n",
    "    'TAA_ratio',\n",
    "    'TGA_ratio',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>organism_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCA_001889405.1</td>\n",
       "      <td>2177</td>\n",
       "      <td>Methanohalophilus halophilus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession  species_taxid                 organism_name\n",
       "0    GCA_001889405.1           2177  Methanohalophilus halophilus"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = \"\"\"\n",
    "select assembly_accession, species_taxid, organism_name\n",
    "from assembly_source \n",
    "where species_taxid = 2177\n",
    "\"\"\"\n",
    "pd.read_sql(q, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>TAG_ratio</th>\n",
       "      <th>TAA_ratio</th>\n",
       "      <th>TGA_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2868</th>\n",
       "      <td>GCA_009730295.1</td>\n",
       "      <td>84135</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.211838</td>\n",
       "      <td>0.769470</td>\n",
       "      <td>0.018692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>GCA_008151785.1</td>\n",
       "      <td>89152</td>\n",
       "      <td>37.000</td>\n",
       "      <td>0.237606</td>\n",
       "      <td>0.741402</td>\n",
       "      <td>0.020992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>GCA_003181555.1</td>\n",
       "      <td>1502</td>\n",
       "      <td>40.333</td>\n",
       "      <td>0.217361</td>\n",
       "      <td>0.753705</td>\n",
       "      <td>0.028934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>GCA_004329605.1</td>\n",
       "      <td>1502</td>\n",
       "      <td>40.333</td>\n",
       "      <td>0.217566</td>\n",
       "      <td>0.751458</td>\n",
       "      <td>0.030977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>GCA_003181895.1</td>\n",
       "      <td>1502</td>\n",
       "      <td>40.333</td>\n",
       "      <td>0.216109</td>\n",
       "      <td>0.752607</td>\n",
       "      <td>0.031284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>GCA_000212695.1</td>\n",
       "      <td>1708</td>\n",
       "      <td>31.000</td>\n",
       "      <td>0.057682</td>\n",
       "      <td>0.007443</td>\n",
       "      <td>0.934875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>GCA_000023205.1</td>\n",
       "      <td>1270</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.037567</td>\n",
       "      <td>0.019678</td>\n",
       "      <td>0.942755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>GCA_001275345.1</td>\n",
       "      <td>71999</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.039391</td>\n",
       "      <td>0.016115</td>\n",
       "      <td>0.944494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>GCA_003355475.1</td>\n",
       "      <td>556288</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0.045198</td>\n",
       "      <td>0.009102</td>\n",
       "      <td>0.945700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>GCA_000023405.1</td>\n",
       "      <td>43669</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.044980</td>\n",
       "      <td>0.008801</td>\n",
       "      <td>0.946219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3186 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     assembly_accession  species_taxid  growth_tmp  TAG_ratio  TAA_ratio  \\\n",
       "2868    GCA_009730295.1          84135      37.000   0.211838   0.769470   \n",
       "2744    GCA_008151785.1          89152      37.000   0.237606   0.741402   \n",
       "2225    GCA_003181555.1           1502      40.333   0.217361   0.753705   \n",
       "2533    GCA_004329605.1           1502      40.333   0.217566   0.751458   \n",
       "2239    GCA_003181895.1           1502      40.333   0.216109   0.752607   \n",
       "...                 ...            ...         ...        ...        ...   \n",
       "732     GCA_000212695.1           1708      31.000   0.057682   0.007443   \n",
       "357     GCA_000023205.1           1270      29.000   0.037567   0.019678   \n",
       "1444    GCA_001275345.1          71999      29.000   0.039391   0.016115   \n",
       "2284    GCA_003355475.1         556288      28.000   0.045198   0.009102   \n",
       "363     GCA_000023405.1          43669      29.000   0.044980   0.008801   \n",
       "\n",
       "      TGA_ratio  \n",
       "2868   0.018692  \n",
       "2744   0.020992  \n",
       "2225   0.028934  \n",
       "2533   0.030977  \n",
       "2239   0.031284  \n",
       "...         ...  \n",
       "732    0.934875  \n",
       "357    0.942755  \n",
       "1444   0.944494  \n",
       "2284   0.945700  \n",
       "363    0.946219  \n",
       "\n",
       "[3186 rows x 6 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codon_df[codon_df['TGA'] > 0].sort_values(['TGA_ratio'])[[\n",
    "    'assembly_accession', \n",
    "    'species_taxid', \n",
    "    'growth_tmp', \n",
    "    'TAG_ratio',\n",
    "    'TAA_ratio',\n",
    "    'TGA_ratio',\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set_df = codon_with_phylogeny_df[codon_with_phylogeny_df['in_test_set']].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are ratios similar across thermophiles / mesophiles / psychrophiles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embedding(x_embedded, colors, cmap='YlOrRd', cbar=True):\n",
    "    _, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    \n",
    "    x = x_embedded[:,0]\n",
    "    y = x_embedded[:,1]\n",
    "    \n",
    "    sc = ax.scatter(x, y, c=colors, cmap=cmap, alpha=0.5)\n",
    "    \n",
    "    if cbar:\n",
    "        plt.colorbar(sc)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1060, 2)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_embedded = TSNE(n_components=2, perplexity=40).fit_transform(x_test)\n",
    "x_test_embedded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_embedding(x_test_embedded, y_test);\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_embedding_per_column(x_embedded, input_df, column, temperature_range=None, min_elements=1):\n",
    "    _, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    \n",
    "    df = input_df\n",
    "    if temperature_range is not None:\n",
    "        df = input_df[\n",
    "            input_df['growth_tmp'].between(*temperature_range)\n",
    "        ]\n",
    "        \n",
    "    df = df.drop_duplicates(['species_taxid'])\n",
    "    \n",
    "    categories = df[df[column].notnull()][column].unique()\n",
    "    \n",
    "    for cat in sorted(categories):\n",
    "        ix = df[df[column] == cat].index\n",
    "        \n",
    "        if len(ix) < min_elements:\n",
    "            continue\n",
    "            \n",
    "        x = x_embedded[ix, 0]\n",
    "        y = x_embedded[ix, 1]\n",
    "        \n",
    "        ax.scatter(x, y, alpha=.8, label=cat)\n",
    "    \n",
    "    ax.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_embedding_per_column(\n",
    "    x_test_embedded, \n",
    "    test_set_df,\n",
    "    column='phylum',\n",
    "    min_elements=3,\n",
    ")\n",
    "ax.set_xlim(xlim);\n",
    "ax.set_ylim(ylim);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_embedding_per_column(\n",
    "    x_test_embedded, \n",
    "    test_set_df,\n",
    "    column='genus',\n",
    "    temperature_range=(60, 120),\n",
    "    min_elements=2,\n",
    ")\n",
    "ax.set_xlim(xlim);\n",
    "ax.set_ylim(ylim);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_heatmap_data_raw(df, ratio_columns, grouping_column, temperature_range=None):\n",
    "    if temperature_range is None:\n",
    "        agg_df = df[\n",
    "            [grouping_column] + ratio_columns\n",
    "        ].groupby(grouping_column).mean().reset_index()\n",
    "    else:\n",
    "        agg_df = df[df['growth_tmp'].between(*temperature_range)][\n",
    "            [grouping_column] + ratio_columns\n",
    "        ].groupby(grouping_column).mean().reset_index()\n",
    "    \n",
    "    data = []\n",
    "    for tpl in agg_df.itertuples():\n",
    "        grouping_column_val = getattr(tpl, grouping_column)\n",
    "        for col in ratio_columns:\n",
    "            codon = col.replace('_ratio', '')\n",
    "            row = [\n",
    "                grouping_column_val,\n",
    "                codon_to_aa_table[codon],\n",
    "                codon,\n",
    "                getattr(tpl, col),\n",
    "            ]\n",
    "            data.append(row)\n",
    "        \n",
    "    output_columns = [\n",
    "        grouping_column,\n",
    "        'aa',\n",
    "        'codon',\n",
    "        'codon_ratio',\n",
    "    ]\n",
    "    return pd.DataFrame(data, columns=output_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_heatmap(\n",
    "    df, \n",
    "    ratio_columns, \n",
    "    grouping_column, \n",
    "    temperature_range, \n",
    "    amino_acids=None, \n",
    "    ax=None, \n",
    "    figsize=None,\n",
    "    sort_master=None,\n",
    "):\n",
    "    heatmap_df_raw = make_heatmap_data_raw(df, ratio_columns, grouping_column, temperature_range)\n",
    "    \n",
    "    if amino_acids is not None:\n",
    "        heatmap_df_raw = heatmap_df_raw[heatmap_df_raw['aa'].isin(amino_acids)].reset_index(drop=True)\n",
    "    \n",
    "    heatmap_df = heatmap_df_raw.pivot(['aa', 'codon'], grouping_column, 'codon_ratio')\n",
    "    \n",
    "    if sort_master is not None:\n",
    "        columns = sorted(\n",
    "            heatmap_df.columns.tolist(),\n",
    "            key=lambda s: sort_master.index(s),\n",
    "        )\n",
    "        heatmap_df.columns = columns\n",
    "    \n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(1, 1, figsize=figsize or (25, 20))\n",
    "        \n",
    "    sns.heatmap(heatmap_df, ax=ax, cmap='Greys')\n",
    "    \n",
    "    return ax, heatmap_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('C', 7.390609225741814),\n",
       " ('I', 7.074987997573898),\n",
       " ('R', 6.128497902148081),\n",
       " ('P', 4.639331818976233),\n",
       " ('S', 4.324754041065607),\n",
       " ('*', 3.9124782796655353),\n",
       " ('G', 3.7105858281695188),\n",
       " ('A', 3.6630624105820284),\n",
       " ('N', 3.637066104298797),\n",
       " ('L', 3.5165006870361193),\n",
       " ('V', 3.1576465337559703),\n",
       " ('Y', 2.4759798750799895),\n",
       " ('H', 2.3976657325528703),\n",
       " ('F', 2.2512273396487776),\n",
       " ('E', 1.976558913808437),\n",
       " ('T', 1.8661235754655932),\n",
       " ('K', 1.6131966375738807),\n",
       " ('Q', 1.5229935480855108),\n",
       " ('D', 1.0941843033942251)]"
      ]
     },
     "execution_count": 707,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted([(k, v) for k, v in aa_importance.items()], key=lambda t: t[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vertical_lines_sep(ax, heatmap_df, dict_map, color, alpha=0.5):\n",
    "    prev = None\n",
    "    for i, species in enumerate(heatmap_df.columns):\n",
    "        v = dict_map[species]\n",
    "        if prev is not None and prev != v:\n",
    "            ax.axvline(i, color=color, alpha=alpha);\n",
    "            prev = v\n",
    "        else:\n",
    "            prev = v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [],
   "source": [
    "bacteria_species = set(test_set_df[test_set_df['superkingdom'] == 'Bacteria']['species'].unique())\n",
    "species_to_phylum = {\n",
    "    species: phylum\n",
    "    for phylum, species in test_set_df[['phylum', 'species']].values\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1709,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "species_sort = [\n",
    "    t[-1]\n",
    "    for t in test_set_df[\n",
    "        ['superkingdom', 'phylum', 'genus', 'species', 'AAA', 'CCC', 'GGG']\n",
    "    ].groupby(['superkingdom', 'phylum','genus', 'species']).mean().index.tolist()\n",
    "]\n",
    "ax, heatmap_df = make_heatmap(\n",
    "    test_set_df, \n",
    "    ratio_columns, \n",
    "    grouping_column='species',\n",
    "    temperature_range=(60, 120),\n",
    "    amino_acids=['C', 'I', 'R', 'P', 'S', '*'],\n",
    "    sort_master=species_sort,\n",
    "    figsize=(25, 8),\n",
    ")\n",
    "\n",
    "prev_aa = None\n",
    "for i, (aa, codon) in enumerate(heatmap_df.index.tolist()):\n",
    "    if prev_aa is not None and aa != prev_aa:\n",
    "        ax.axhline(i, color='black', alpha=0.4);\n",
    "        prev_aa = aa\n",
    "    else:\n",
    "        prev_aa = aa\n",
    "        \n",
    "vertical_lines_sep(ax, heatmap_df, species_to_phylum, color='black', alpha=0.4);\n",
    "\n",
    "for i, species in enumerate(heatmap_df.columns):\n",
    "    if species in bacteria_species:\n",
    "        ax.axvline(i, color='black', alpha=0.4, linewidth=3);\n",
    "        break\n",
    "        \n",
    "ax.set_title('Codon bias distribution across species\\n');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute distance between species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1379,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kl_divergence(p, q, axis=0):\n",
    "    return entropy(p, q, axis=axis)\n",
    "\n",
    "\n",
    "def jensen_shannon_divergence(p, q, axis=0):\n",
    "    m = (1/2) * (p + q)\n",
    "    return (\n",
    "        (1/2) * kl_divergence(p, m, axis) + \n",
    "        (1/2) * kl_divergence(q, m, axis)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance_between_species(df, species_1, species_2, weights_dict=None):\n",
    "    agg_df = df[df['species'].isin([\n",
    "        species_1, \n",
    "        species_2,\n",
    "    ])][['species_taxid', 'species'] + ratio_columns].groupby(['species_taxid', 'species']).mean()\n",
    "    \n",
    "    measures = []\n",
    "    weights = []\n",
    "    \n",
    "    seen_codons = set()\n",
    "    for c in possible_codons:\n",
    "        if c in seen_codons:\n",
    "            continue\n",
    "    \n",
    "        redundant_codons = [cod for cod, _ in CODON_REDUNDANCY[c]]\n",
    "        if len(redundant_codons) < 2:\n",
    "            seen_codons.add(c)\n",
    "            continue\n",
    "            \n",
    "        aa = codon_to_aa_table[c]\n",
    "        \n",
    "        p = np.zeros((len(redundant_codons),))\n",
    "        q = np.zeros((len(redundant_codons),))\n",
    "        for i, codon in enumerate(redundant_codons):\n",
    "            seen_codons.add(codon)\n",
    "            pi, qi = agg_df[f'{codon}_ratio'].values\n",
    "            p[i] = pi\n",
    "            q[i] = qi\n",
    "        \n",
    "        if weights_dict is not None:\n",
    "            weights.append(weights_dict[aa])\n",
    "            \n",
    "        measures.append(\n",
    "            jensen_shannon_divergence(p, q)\n",
    "        )\n",
    "        \n",
    "    if weights_dict is None:\n",
    "        return np.mean(measures)\n",
    "    else:\n",
    "        weights = np.array(weights) / np.sum(weights)\n",
    "        return np.average(measures, weights=weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04378984069228989"
      ]
     },
     "execution_count": 889,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_between_species(test_set_df, 'Thermococcus radiotolerans', 'Thermotoga maritima')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07751900953905895"
      ]
     },
     "execution_count": 890,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_between_species(test_set_df, 'Thermosipho melanesiensis', 'Thermotoga maritima')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.054759904766251886"
      ]
     },
     "execution_count": 891,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_between_species(test_set_df, 'Azorhizobium caulinodans', 'Thermococcus radiotolerans')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02250581778325244"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_between_species(test_set_df, 'Thermococcus radiotolerans', 'Desulfurococcus mucosus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>species</th>\n",
       "      <th>growth_tmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2336</td>\n",
       "      <td>Thermotoga maritima</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>46541</td>\n",
       "      <td>Thermosipho melanesiensis</td>\n",
       "      <td>69.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2275</td>\n",
       "      <td>Desulfurococcus mucosus</td>\n",
       "      <td>87.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>187880</td>\n",
       "      <td>Thermococcus radiotolerans</td>\n",
       "      <td>88.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species_taxid                     species  growth_tmp\n",
       "16            2336         Thermotoga maritima       80.00\n",
       "56           46541   Thermosipho melanesiensis       69.95\n",
       "147           2275     Desulfurococcus mucosus       87.00\n",
       "538         187880  Thermococcus radiotolerans       88.00"
      ]
     },
     "execution_count": 897,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set_df[test_set_df['species'].isin([\n",
    "    'Thermococcus radiotolerans', 'Thermotoga maritima', 'Desulfurococcus mucosus', 'Thermosipho melanesiensis'\n",
    "])][['species_taxid', 'species', 'growth_tmp']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>in_test_set</th>\n",
       "      <th>AAA_ratio</th>\n",
       "      <th>AAG_ratio</th>\n",
       "      <th>AAT_ratio</th>\n",
       "      <th>AAC_ratio</th>\n",
       "      <th>ACT_ratio</th>\n",
       "      <th>ACC_ratio</th>\n",
       "      <th>ACA_ratio</th>\n",
       "      <th>ACG_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>TTC_ratio</th>\n",
       "      <th>TTT_ratio</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "      <td>0.082550</td>\n",
       "      <td>0.917450</td>\n",
       "      <td>0.327724</td>\n",
       "      <td>0.672276</td>\n",
       "      <td>0.024511</td>\n",
       "      <td>0.575353</td>\n",
       "      <td>0.036262</td>\n",
       "      <td>0.363875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.897180</td>\n",
       "      <td>0.102820</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Azorhizobium caulinodans</td>\n",
       "      <td>Azorhizobium</td>\n",
       "      <td>Xanthobacteraceae</td>\n",
       "      <td>Rhizobiales</td>\n",
       "      <td>Alphaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>0.919627</td>\n",
       "      <td>0.080373</td>\n",
       "      <td>0.858168</td>\n",
       "      <td>0.141832</td>\n",
       "      <td>0.454569</td>\n",
       "      <td>0.050026</td>\n",
       "      <td>0.446018</td>\n",
       "      <td>0.049387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080224</td>\n",
       "      <td>0.919776</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Buchnera aphidicola</td>\n",
       "      <td>Buchnera</td>\n",
       "      <td>Erwiniaceae</td>\n",
       "      <td>Enterobacterales</td>\n",
       "      <td>Gammaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>0.007302</td>\n",
       "      <td>0.992698</td>\n",
       "      <td>0.010730</td>\n",
       "      <td>0.989270</td>\n",
       "      <td>0.008586</td>\n",
       "      <td>0.425527</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.550098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994594</td>\n",
       "      <td>0.005406</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Cellulomonas gilvus</td>\n",
       "      <td>Cellulomonas</td>\n",
       "      <td>Cellulomonadaceae</td>\n",
       "      <td>Micrococcales</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>True</td>\n",
       "      <td>0.664866</td>\n",
       "      <td>0.335134</td>\n",
       "      <td>0.775571</td>\n",
       "      <td>0.224429</td>\n",
       "      <td>0.458203</td>\n",
       "      <td>0.188052</td>\n",
       "      <td>0.304183</td>\n",
       "      <td>0.049563</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189441</td>\n",
       "      <td>0.810559</td>\n",
       "      <td>74.15</td>\n",
       "      <td>Dictyoglomus thermophilum</td>\n",
       "      <td>Dictyoglomus</td>\n",
       "      <td>Dictyoglomaceae</td>\n",
       "      <td>Dictyoglomales</td>\n",
       "      <td>Dictyoglomia</td>\n",
       "      <td>Dictyoglomi</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>True</td>\n",
       "      <td>0.551810</td>\n",
       "      <td>0.448190</td>\n",
       "      <td>0.440559</td>\n",
       "      <td>0.559441</td>\n",
       "      <td>0.099134</td>\n",
       "      <td>0.591478</td>\n",
       "      <td>0.097796</td>\n",
       "      <td>0.211592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.502549</td>\n",
       "      <td>0.497451</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Pelobacter carbinolicus</td>\n",
       "      <td>Pelobacter</td>\n",
       "      <td>Desulfuromonadaceae</td>\n",
       "      <td>Desulfuromonadales</td>\n",
       "      <td>Deltaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   species_taxid  in_test_set  AAA_ratio  AAG_ratio  AAT_ratio  AAC_ratio  \\\n",
       "0              7         True   0.082550   0.917450   0.327724   0.672276   \n",
       "1              9        False   0.919627   0.080373   0.858168   0.141832   \n",
       "2             11         True   0.007302   0.992698   0.010730   0.989270   \n",
       "3             14         True   0.664866   0.335134   0.775571   0.224429   \n",
       "4             19         True   0.551810   0.448190   0.440559   0.559441   \n",
       "\n",
       "   ACT_ratio  ACC_ratio  ACA_ratio  ACG_ratio  ...  TTC_ratio  TTT_ratio  \\\n",
       "0   0.024511   0.575353   0.036262   0.363875  ...   0.897180   0.102820   \n",
       "1   0.454569   0.050026   0.446018   0.049387  ...   0.080224   0.919776   \n",
       "2   0.008586   0.425527   0.015789   0.550098  ...   0.994594   0.005406   \n",
       "3   0.458203   0.188052   0.304183   0.049563  ...   0.189441   0.810559   \n",
       "4   0.099134   0.591478   0.097796   0.211592  ...   0.502549   0.497451   \n",
       "\n",
       "   growth_tmp                    species         genus               family  \\\n",
       "0       30.00   Azorhizobium caulinodans  Azorhizobium    Xanthobacteraceae   \n",
       "1       24.00        Buchnera aphidicola      Buchnera          Erwiniaceae   \n",
       "2       26.00        Cellulomonas gilvus  Cellulomonas    Cellulomonadaceae   \n",
       "3       74.15  Dictyoglomus thermophilum  Dictyoglomus      Dictyoglomaceae   \n",
       "4       30.00    Pelobacter carbinolicus    Pelobacter  Desulfuromonadaceae   \n",
       "\n",
       "                order                class          phylum  superkingdom  \n",
       "0         Rhizobiales  Alphaproteobacteria  Proteobacteria      Bacteria  \n",
       "1    Enterobacterales  Gammaproteobacteria  Proteobacteria      Bacteria  \n",
       "2       Micrococcales       Actinobacteria  Actinobacteria      Bacteria  \n",
       "3      Dictyoglomales         Dictyoglomia     Dictyoglomi      Bacteria  \n",
       "4  Desulfuromonadales  Deltaproteobacteria  Proteobacteria      Bacteria  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 1044,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_codon_ratios_path = os.path.join(os.getcwd(), 'data/species_codon_ratios.csv')\n",
    "\n",
    "q = 'select species_taxid, species, genus, family, \"order\", class, phylum, superkingdom from species_traits'\n",
    "\n",
    "species_codon_df = pd.merge(\n",
    "    pd.read_csv(species_codon_ratios_path),\n",
    "    species_temperatures_df,\n",
    "    how='inner',\n",
    "    on='species_taxid',\n",
    ")\n",
    "species_codon_df = pd.merge(\n",
    "    species_codon_df,\n",
    "    pd.read_sql(q, engine),\n",
    "    how='inner',\n",
    "    on='species_taxid',\n",
    ")\n",
    "species_codon_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1589,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2680, 2680)"
      ]
     },
     "execution_count": 1589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_matrix_path = os.path.join(os.getcwd(), 'data/distance_matrix.npy')\n",
    "distance_matrix = np.load(distance_matrix_path, allow_pickle=True)\n",
    "distance_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(682, 1769, 0.1956751534857982, 0.1956751534857982)"
      ]
     },
     "execution_count": 1595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = species_codon_df[species_codon_df['species_taxid'].isin((2336, 187880))].index\n",
    "ix[0], ix[-1], distance_matrix[ix[0], ix[-1]], distance_matrix[ix[-1], ix[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1596,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_appropriate_threshold_distance(species_codon_df, distance_matrix):    \n",
    "    output_columns = [\n",
    "        'species_taxid',\n",
    "        'n_neighbours',\n",
    "        'distance_mean',\n",
    "        'distance_std',\n",
    "        'distance_min',\n",
    "        'distance_max',\n",
    "    ]\n",
    "    \n",
    "    data = []\n",
    "    for ix in range(len(species_codon_df)):\n",
    "        species = species_codon_df.loc[ix]\n",
    "\n",
    "        d = [\n",
    "            v for i, v in enumerate(distance_matrix[ix])\n",
    "            if i != ix\n",
    "        ]\n",
    "\n",
    "        data.append([\n",
    "            species['species_taxid'],\n",
    "            len(d),\n",
    "            np.mean(d) if len(d) > 0 else 0.,\n",
    "            np.std(d) if len(d) > 0 else 0.,\n",
    "            np.min(d) if len(d) > 0 else 0.,\n",
    "            np.max(d) if len(d) > 0 else 0.,\n",
    "        ])\n",
    "        \n",
    "    distance_df = pd.DataFrame(data, columns=output_columns)\n",
    "    \n",
    "    return (distance_df['distance_mean'] - 1.1 * distance_df['distance_std']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1597,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1230565327515076"
      ]
     },
     "execution_count": 1597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_distance = compute_appropriate_threshold_distance(species_codon_df, distance_matrix)\n",
    "threshold_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_neighbours(species_codon_df, distance_matrix, species_taxid, max_distance):\n",
    "    index = species_codon_df[species_codon_df['species_taxid'] == species_taxid].index\n",
    "    if len(index) == 0:\n",
    "        raise ValueError(f'No species foudn with id {species_taxid}')\n",
    "        \n",
    "    ix = index[0]\n",
    "    \n",
    "    #distances = np.delete(distance_matrix[ix], ix)\n",
    "    \n",
    "    #mean = distances.mean()\n",
    "    #std = distances.std()\n",
    "    \n",
    "    #threshold = mean - n_stds * std\n",
    "    \n",
    "    indices = [\n",
    "        i for i, v in enumerate(distance_matrix[ix])\n",
    "        if i != ix and v <= max_distance\n",
    "    ]\n",
    "    d = [\n",
    "        v for i, v in enumerate(distance_matrix[ix])\n",
    "        if i != ix and v <= max_distance\n",
    "    ]\n",
    "    \n",
    "    df = species_codon_df.loc[indices]\n",
    "    df['distance'] = d\n",
    "    \n",
    "    return df.sort_values('distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>in_test_set</th>\n",
       "      <th>AAA_ratio</th>\n",
       "      <th>AAG_ratio</th>\n",
       "      <th>AAT_ratio</th>\n",
       "      <th>AAC_ratio</th>\n",
       "      <th>ACT_ratio</th>\n",
       "      <th>ACC_ratio</th>\n",
       "      <th>ACA_ratio</th>\n",
       "      <th>ACG_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>TTT_ratio</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>93930</td>\n",
       "      <td>False</td>\n",
       "      <td>0.557737</td>\n",
       "      <td>0.442263</td>\n",
       "      <td>0.299199</td>\n",
       "      <td>0.700801</td>\n",
       "      <td>0.165036</td>\n",
       "      <td>0.273389</td>\n",
       "      <td>0.284350</td>\n",
       "      <td>0.277225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368468</td>\n",
       "      <td>77.15</td>\n",
       "      <td>Thermotoga naphthophila</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>93929</td>\n",
       "      <td>False</td>\n",
       "      <td>0.560668</td>\n",
       "      <td>0.439332</td>\n",
       "      <td>0.307696</td>\n",
       "      <td>0.692304</td>\n",
       "      <td>0.165958</td>\n",
       "      <td>0.274580</td>\n",
       "      <td>0.283657</td>\n",
       "      <td>0.275805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368234</td>\n",
       "      <td>77.25</td>\n",
       "      <td>Thermotoga petrophila</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>2337</td>\n",
       "      <td>False</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>0.469372</td>\n",
       "      <td>0.247403</td>\n",
       "      <td>0.752597</td>\n",
       "      <td>0.124817</td>\n",
       "      <td>0.303189</td>\n",
       "      <td>0.283571</td>\n",
       "      <td>0.288422</td>\n",
       "      <td>...</td>\n",
       "      <td>0.387126</td>\n",
       "      <td>79.00</td>\n",
       "      <td>Thermotoga neapolitana</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.001007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      species_taxid  in_test_set  AAA_ratio  AAG_ratio  AAT_ratio  AAC_ratio  \\\n",
       "1464          93930        False   0.557737   0.442263   0.299199   0.700801   \n",
       "1463          93929        False   0.560668   0.439332   0.307696   0.692304   \n",
       "683            2337        False   0.530628   0.469372   0.247403   0.752597   \n",
       "\n",
       "      ACT_ratio  ACC_ratio  ACA_ratio  ACG_ratio  ...  TTT_ratio  growth_tmp  \\\n",
       "1464   0.165036   0.273389   0.284350   0.277225  ...   0.368468       77.15   \n",
       "1463   0.165958   0.274580   0.283657   0.275805  ...   0.368234       77.25   \n",
       "683    0.124817   0.303189   0.283571   0.288422  ...   0.387126       79.00   \n",
       "\n",
       "                      species       genus          family          order  \\\n",
       "1464  Thermotoga naphthophila  Thermotoga  Thermotogaceae  Thermotogales   \n",
       "1463    Thermotoga petrophila  Thermotoga  Thermotogaceae  Thermotogales   \n",
       "683    Thermotoga neapolitana  Thermotoga  Thermotogaceae  Thermotogales   \n",
       "\n",
       "            class       phylum  superkingdom  distance  \n",
       "1464  Thermotogae  Thermotogae      Bacteria  0.000010  \n",
       "1463  Thermotogae  Thermotogae      Bacteria  0.000015  \n",
       "683   Thermotogae  Thermotogae      Bacteria  0.001007  \n",
       "\n",
       "[3 rows x 73 columns]"
      ]
     },
     "execution_count": 1262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_neighbours(species_codon_df, distance_matrix, species_taxid=2336, max_distance=threshold_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 1180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(get_neighbours(species_codon_df, distance_matrix, species_taxid=58290, max_distance=threshold_distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_neighbours_stats(species_codon_df, distance_matrix, max_distance):    \n",
    "    output_columns = [\n",
    "        'species_taxid',\n",
    "        'growth_tmp',\n",
    "        'species', \n",
    "        'genus', \n",
    "        'family', \n",
    "        'order',\n",
    "        'class', \n",
    "        'phylum', \n",
    "        'superkingdom',\n",
    "        'n_neighbours',\n",
    "        'distance_mean',\n",
    "        'distance_std',\n",
    "        'distance_min',\n",
    "        'distance_max',\n",
    "    ]\n",
    "    \n",
    "    data = []\n",
    "    for ix in range(len(species_codon_df)):\n",
    "        species = species_codon_df.loc[ix]\n",
    "\n",
    "        d = [\n",
    "            v for i, v in enumerate(distance_matrix[ix])\n",
    "            if i != ix and v <= max_distance\n",
    "        ]\n",
    "\n",
    "        data.append([\n",
    "            species['species_taxid'],\n",
    "            species['growth_tmp'],\n",
    "            species['species'], \n",
    "            species['genus'], \n",
    "            species['family'], \n",
    "            species['order'],\n",
    "            species['class'], \n",
    "            species['phylum'], \n",
    "            species['superkingdom'],\n",
    "            len(d),\n",
    "            np.mean(d) if len(d) > 0 else 0.,\n",
    "            np.std(d) if len(d) > 0 else 0.,\n",
    "            np.min(d) if len(d) > 0 else 0.,\n",
    "            np.max(d) if len(d) > 0 else 0.,\n",
    "        ])\n",
    "        \n",
    "    return pd.DataFrame(data, columns=output_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>n_neighbours</th>\n",
       "      <th>distance_mean</th>\n",
       "      <th>distance_std</th>\n",
       "      <th>distance_min</th>\n",
       "      <th>distance_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Azorhizobium caulinodans</td>\n",
       "      <td>Azorhizobium</td>\n",
       "      <td>Xanthobacteraceae</td>\n",
       "      <td>Rhizobiales</td>\n",
       "      <td>Alphaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>142</td>\n",
       "      <td>0.009575</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.001159</td>\n",
       "      <td>0.012413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>24.00</td>\n",
       "      <td>Buchnera aphidicola</td>\n",
       "      <td>Buchnera</td>\n",
       "      <td>Erwiniaceae</td>\n",
       "      <td>Enterobacterales</td>\n",
       "      <td>Gammaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>53</td>\n",
       "      <td>0.010796</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.012408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>26.00</td>\n",
       "      <td>Cellulomonas gilvus</td>\n",
       "      <td>Cellulomonas</td>\n",
       "      <td>Cellulomonadaceae</td>\n",
       "      <td>Micrococcales</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Actinobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>121</td>\n",
       "      <td>0.008754</td>\n",
       "      <td>0.002545</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.012381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>74.15</td>\n",
       "      <td>Dictyoglomus thermophilum</td>\n",
       "      <td>Dictyoglomus</td>\n",
       "      <td>Dictyoglomaceae</td>\n",
       "      <td>Dictyoglomales</td>\n",
       "      <td>Dictyoglomia</td>\n",
       "      <td>Dictyoglomi</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>104</td>\n",
       "      <td>0.008991</td>\n",
       "      <td>0.002246</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.012370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Pelobacter carbinolicus</td>\n",
       "      <td>Pelobacter</td>\n",
       "      <td>Desulfuromonadaceae</td>\n",
       "      <td>Desulfuromonadales</td>\n",
       "      <td>Deltaproteobacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>264</td>\n",
       "      <td>0.009170</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.001609</td>\n",
       "      <td>0.012411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   species_taxid  growth_tmp                    species         genus  \\\n",
       "0              7       30.00   Azorhizobium caulinodans  Azorhizobium   \n",
       "1              9       24.00        Buchnera aphidicola      Buchnera   \n",
       "2             11       26.00        Cellulomonas gilvus  Cellulomonas   \n",
       "3             14       74.15  Dictyoglomus thermophilum  Dictyoglomus   \n",
       "4             19       30.00    Pelobacter carbinolicus    Pelobacter   \n",
       "\n",
       "                family               order                class  \\\n",
       "0    Xanthobacteraceae         Rhizobiales  Alphaproteobacteria   \n",
       "1          Erwiniaceae    Enterobacterales  Gammaproteobacteria   \n",
       "2    Cellulomonadaceae       Micrococcales       Actinobacteria   \n",
       "3      Dictyoglomaceae      Dictyoglomales         Dictyoglomia   \n",
       "4  Desulfuromonadaceae  Desulfuromonadales  Deltaproteobacteria   \n",
       "\n",
       "           phylum superkingdom  n_neighbours  distance_mean  distance_std  \\\n",
       "0  Proteobacteria     Bacteria           142       0.009575      0.002239   \n",
       "1  Proteobacteria     Bacteria            53       0.010796      0.001087   \n",
       "2  Actinobacteria     Bacteria           121       0.008754      0.002545   \n",
       "3     Dictyoglomi     Bacteria           104       0.008991      0.002246   \n",
       "4  Proteobacteria     Bacteria           264       0.009170      0.002240   \n",
       "\n",
       "   distance_min  distance_max  \n",
       "0      0.001159      0.012413  \n",
       "1      0.008200      0.012408  \n",
       "2      0.000537      0.012381  \n",
       "3      0.000310      0.012370  \n",
       "4      0.001609      0.012411  "
      ]
     },
     "execution_count": 1182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df = get_neighbours_stats(species_codon_df, distance_matrix, max_distance=threshold_distance)\n",
    "stats_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159.64328358208957, 104.57317749286152, 0, 428)"
      ]
     },
     "execution_count": 1183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    stats_df['n_neighbours'].mean(), \n",
    "    stats_df['n_neighbours'].std(), \n",
    "    stats_df['n_neighbours'].min(), \n",
    "    stats_df['n_neighbours'].max(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>n_neighbours</th>\n",
       "      <th>distance_mean</th>\n",
       "      <th>distance_std</th>\n",
       "      <th>distance_min</th>\n",
       "      <th>distance_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>2336</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Thermotoga maritima</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>8</td>\n",
       "      <td>0.006247</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.011454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     species_taxid  growth_tmp              species       genus  \\\n",
       "682           2336        80.0  Thermotoga maritima  Thermotoga   \n",
       "\n",
       "             family          order        class       phylum superkingdom  \\\n",
       "682  Thermotogaceae  Thermotogales  Thermotogae  Thermotogae     Bacteria   \n",
       "\n",
       "     n_neighbours  distance_mean  distance_std  distance_min  distance_max  \n",
       "682             8       0.006247      0.004722       0.00001      0.011454  "
      ]
     },
     "execution_count": 1184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df[stats_df['species_taxid'] == 2336]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 1185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stats_df[stats_df['n_neighbours'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67.85, 77.15308270955374)"
      ]
     },
     "execution_count": 1219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbour_df = get_neighbours(species_codon_df, distance_matrix, species_taxid=2336, max_distance=threshold_distance)\n",
    "d = neighbour_df['distance']\n",
    "d = 1 / (d / np.sum(d))\n",
    "d = d / np.sum(d)\n",
    "neighbour_df['p'] = d\n",
    "neighbour_df['growth_tmp'].mean(), np.average(neighbour_df['growth_tmp'].values, weights=neighbour_df['p'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species_taxid</th>\n",
       "      <th>in_test_set</th>\n",
       "      <th>AAA_ratio</th>\n",
       "      <th>AAG_ratio</th>\n",
       "      <th>AAT_ratio</th>\n",
       "      <th>AAC_ratio</th>\n",
       "      <th>ACT_ratio</th>\n",
       "      <th>ACC_ratio</th>\n",
       "      <th>ACA_ratio</th>\n",
       "      <th>ACG_ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>growth_tmp</th>\n",
       "      <th>species</th>\n",
       "      <th>genus</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>class</th>\n",
       "      <th>phylum</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>distance</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>93930</td>\n",
       "      <td>False</td>\n",
       "      <td>0.557737</td>\n",
       "      <td>0.442263</td>\n",
       "      <td>0.299199</td>\n",
       "      <td>0.700801</td>\n",
       "      <td>0.165036</td>\n",
       "      <td>0.273389</td>\n",
       "      <td>0.284350</td>\n",
       "      <td>0.277225</td>\n",
       "      <td>...</td>\n",
       "      <td>77.15</td>\n",
       "      <td>Thermotoga naphthophila</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.585270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>93929</td>\n",
       "      <td>False</td>\n",
       "      <td>0.560668</td>\n",
       "      <td>0.439332</td>\n",
       "      <td>0.307696</td>\n",
       "      <td>0.692304</td>\n",
       "      <td>0.165958</td>\n",
       "      <td>0.274580</td>\n",
       "      <td>0.283657</td>\n",
       "      <td>0.275805</td>\n",
       "      <td>...</td>\n",
       "      <td>77.25</td>\n",
       "      <td>Thermotoga petrophila</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.405651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>2337</td>\n",
       "      <td>False</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>0.469372</td>\n",
       "      <td>0.247403</td>\n",
       "      <td>0.752597</td>\n",
       "      <td>0.124817</td>\n",
       "      <td>0.303189</td>\n",
       "      <td>0.283571</td>\n",
       "      <td>0.288422</td>\n",
       "      <td>...</td>\n",
       "      <td>79.00</td>\n",
       "      <td>Thermotoga neapolitana</td>\n",
       "      <td>Thermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.001007</td>\n",
       "      <td>0.005949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>57487</td>\n",
       "      <td>False</td>\n",
       "      <td>0.433576</td>\n",
       "      <td>0.566424</td>\n",
       "      <td>0.215778</td>\n",
       "      <td>0.784222</td>\n",
       "      <td>0.148958</td>\n",
       "      <td>0.266927</td>\n",
       "      <td>0.191408</td>\n",
       "      <td>0.392707</td>\n",
       "      <td>...</td>\n",
       "      <td>69.95</td>\n",
       "      <td>Pseudothermotoga hypogea</td>\n",
       "      <td>Pseudothermotoga</td>\n",
       "      <td>Thermotogaceae</td>\n",
       "      <td>Thermotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.008109</td>\n",
       "      <td>0.000739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>1236046</td>\n",
       "      <td>False</td>\n",
       "      <td>0.474313</td>\n",
       "      <td>0.525687</td>\n",
       "      <td>0.349803</td>\n",
       "      <td>0.650197</td>\n",
       "      <td>0.209594</td>\n",
       "      <td>0.330351</td>\n",
       "      <td>0.193856</td>\n",
       "      <td>0.266199</td>\n",
       "      <td>...</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Mesotoga infera</td>\n",
       "      <td>Mesotoga</td>\n",
       "      <td>Kosmotogaceae</td>\n",
       "      <td>Kosmotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>0.000735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>565033</td>\n",
       "      <td>True</td>\n",
       "      <td>0.475462</td>\n",
       "      <td>0.524538</td>\n",
       "      <td>0.404465</td>\n",
       "      <td>0.595535</td>\n",
       "      <td>0.179864</td>\n",
       "      <td>0.270949</td>\n",
       "      <td>0.304756</td>\n",
       "      <td>0.244431</td>\n",
       "      <td>...</td>\n",
       "      <td>80.00</td>\n",
       "      <td>Geoglobus acetivorans</td>\n",
       "      <td>Geoglobus</td>\n",
       "      <td>Archaeoglobaceae</td>\n",
       "      <td>Archaeoglobales</td>\n",
       "      <td>Archaeoglobi</td>\n",
       "      <td>Euryarchaeota</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>0.009987</td>\n",
       "      <td>0.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>1184387</td>\n",
       "      <td>False</td>\n",
       "      <td>0.412678</td>\n",
       "      <td>0.587322</td>\n",
       "      <td>0.476407</td>\n",
       "      <td>0.523593</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>0.229309</td>\n",
       "      <td>0.264346</td>\n",
       "      <td>0.223012</td>\n",
       "      <td>...</td>\n",
       "      <td>37.00</td>\n",
       "      <td>Mesotoga prima</td>\n",
       "      <td>Mesotoga</td>\n",
       "      <td>Kosmotogaceae</td>\n",
       "      <td>Kosmotogales</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Thermotogae</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>0.011247</td>\n",
       "      <td>0.000533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>58290</td>\n",
       "      <td>False</td>\n",
       "      <td>0.389784</td>\n",
       "      <td>0.610216</td>\n",
       "      <td>0.327569</td>\n",
       "      <td>0.672431</td>\n",
       "      <td>0.206981</td>\n",
       "      <td>0.202531</td>\n",
       "      <td>0.281241</td>\n",
       "      <td>0.309247</td>\n",
       "      <td>...</td>\n",
       "      <td>77.45</td>\n",
       "      <td>Archaeoglobus veneficus</td>\n",
       "      <td>Archaeoglobus</td>\n",
       "      <td>Archaeoglobaceae</td>\n",
       "      <td>Archaeoglobales</td>\n",
       "      <td>Archaeoglobi</td>\n",
       "      <td>Euryarchaeota</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>0.011454</td>\n",
       "      <td>0.000523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      species_taxid  in_test_set  AAA_ratio  AAG_ratio  AAT_ratio  AAC_ratio  \\\n",
       "1464          93930        False   0.557737   0.442263   0.299199   0.700801   \n",
       "1463          93929        False   0.560668   0.439332   0.307696   0.692304   \n",
       "683            2337        False   0.530628   0.469372   0.247403   0.752597   \n",
       "1190          57487        False   0.433576   0.566424   0.215778   0.784222   \n",
       "2558        1236046        False   0.474313   0.525687   0.349803   0.650197   \n",
       "2334         565033         True   0.475462   0.524538   0.404465   0.595535   \n",
       "2529        1184387        False   0.412678   0.587322   0.476407   0.523593   \n",
       "1201          58290        False   0.389784   0.610216   0.327569   0.672431   \n",
       "\n",
       "      ACT_ratio  ACC_ratio  ACA_ratio  ACG_ratio  ...  growth_tmp  \\\n",
       "1464   0.165036   0.273389   0.284350   0.277225  ...       77.15   \n",
       "1463   0.165958   0.274580   0.283657   0.275805  ...       77.25   \n",
       "683    0.124817   0.303189   0.283571   0.288422  ...       79.00   \n",
       "1190   0.148958   0.266927   0.191408   0.392707  ...       69.95   \n",
       "2558   0.209594   0.330351   0.193856   0.266199  ...       45.00   \n",
       "2334   0.179864   0.270949   0.304756   0.244431  ...       80.00   \n",
       "2529   0.283333   0.229309   0.264346   0.223012  ...       37.00   \n",
       "1201   0.206981   0.202531   0.281241   0.309247  ...       77.45   \n",
       "\n",
       "                       species             genus            family  \\\n",
       "1464   Thermotoga naphthophila        Thermotoga    Thermotogaceae   \n",
       "1463     Thermotoga petrophila        Thermotoga    Thermotogaceae   \n",
       "683     Thermotoga neapolitana        Thermotoga    Thermotogaceae   \n",
       "1190  Pseudothermotoga hypogea  Pseudothermotoga    Thermotogaceae   \n",
       "2558           Mesotoga infera          Mesotoga     Kosmotogaceae   \n",
       "2334     Geoglobus acetivorans         Geoglobus  Archaeoglobaceae   \n",
       "2529            Mesotoga prima          Mesotoga     Kosmotogaceae   \n",
       "1201   Archaeoglobus veneficus     Archaeoglobus  Archaeoglobaceae   \n",
       "\n",
       "                order         class         phylum  superkingdom  distance  \\\n",
       "1464    Thermotogales   Thermotogae    Thermotogae      Bacteria  0.000010   \n",
       "1463    Thermotogales   Thermotogae    Thermotogae      Bacteria  0.000015   \n",
       "683     Thermotogales   Thermotogae    Thermotogae      Bacteria  0.001007   \n",
       "1190    Thermotogales   Thermotogae    Thermotogae      Bacteria  0.008109   \n",
       "2558     Kosmotogales   Thermotogae    Thermotogae      Bacteria  0.008150   \n",
       "2334  Archaeoglobales  Archaeoglobi  Euryarchaeota       Archaea  0.009987   \n",
       "2529     Kosmotogales   Thermotogae    Thermotogae      Bacteria  0.011247   \n",
       "1201  Archaeoglobales  Archaeoglobi  Euryarchaeota       Archaea  0.011454   \n",
       "\n",
       "             p  \n",
       "1464  0.585270  \n",
       "1463  0.405651  \n",
       "683   0.005949  \n",
       "1190  0.000739  \n",
       "2558  0.000735  \n",
       "2334  0.000600  \n",
       "2529  0.000533  \n",
       "1201  0.000523  \n",
       "\n",
       "[8 rows x 74 columns]"
      ]
     },
     "execution_count": 1350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbour_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, heatmap_df = make_heatmap(\n",
    "    species_codon_df[\n",
    "        species_codon_df['species_taxid'].isin(\n",
    "            neighbour_df['species_taxid'].values.tolist() + [2336]\n",
    "        )\n",
    "    ], \n",
    "    ratio_columns, \n",
    "    grouping_column='species',\n",
    "    temperature_range=(0, 120),\n",
    "    figsize=(20, 20),\n",
    ")\n",
    "\n",
    "prev_aa = None\n",
    "for i, (aa, codon) in enumerate(heatmap_df.index.tolist()):\n",
    "    if prev_aa is not None and aa != prev_aa:\n",
    "        ax.axhline(i, color='black', alpha=0.6);\n",
    "        prev_aa = aa\n",
    "    else:\n",
    "        prev_aa = aa\n",
    "\n",
    "species_to_phylum = {\n",
    "    species: phylum\n",
    "    for phylum, species in species_codon_df[['phylum', 'species']].values\n",
    "}\n",
    "vertical_lines_sep(ax, heatmap_df, species_to_phylum, color='black', alpha=0.4);\n",
    "\n",
    "for i, species in enumerate(heatmap_df.columns):\n",
    "    if species in bacteria_species:\n",
    "        ax.axvline(i, color='black', alpha=0.6, linewidth=3);\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_correlated_aa_from_neighbours(species_codon_df, distance_matrix, ratio_columns, species_taxid, max_distance):\n",
    "    index = species_codon_df[species_codon_df['species_taxid'] == species_taxid].index\n",
    "    if len(index) == 0:\n",
    "        raise ValueError(f'No species foudn with id {species_taxid}')\n",
    "        \n",
    "    ix = index[0]\n",
    "    \n",
    "    other_ix = [\n",
    "        i for i, v in enumerate(distance_matrix[ix])\n",
    "        if i != ix and v <= max_distance\n",
    "    ]\n",
    "    \n",
    "    all_measures = []\n",
    "    aa_list = []\n",
    "    for other_index in other_ix:\n",
    "        measures = []\n",
    "        seen_codons = set()\n",
    "        for c in possible_codons:\n",
    "            if c in seen_codons:\n",
    "                continue\n",
    "\n",
    "            redundant_codons = [cod for cod, _ in CODON_REDUNDANCY[c]]\n",
    "            if len(redundant_codons) < 2:\n",
    "                seen_codons.add(c)\n",
    "                continue\n",
    "\n",
    "            aa = codon_to_aa_table[c]\n",
    "            aa_list.append(aa)\n",
    "\n",
    "            p = np.zeros((len(redundant_codons),))\n",
    "            q = np.zeros((len(redundant_codons),))\n",
    "            for i, codon in enumerate(redundant_codons):\n",
    "                seen_codons.add(codon)\n",
    "                pi, qi = species_codon_df.loc[[ix, other_index]][f'{codon}_ratio'].values\n",
    "                p[i] = pi\n",
    "                q[i] = qi\n",
    "\n",
    "            measures.append(\n",
    "                jensen_shannon_divergence(p, q)\n",
    "            )\n",
    "        \n",
    "        all_measures.append(measures)\n",
    "        \n",
    "    avg_mesures = np.mean(all_measures, axis=0)\n",
    "    \n",
    "    return sorted(\n",
    "        [\n",
    "            (aa_list[i], avg_mesures[i])\n",
    "            for i in range(len(avg_mesures))\n",
    "        ],\n",
    "        key=lambda t: t[1],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('D', 0.0007799467269615005),\n",
       " ('F', 0.0014119098144310918),\n",
       " ('P', 0.002031620519664627),\n",
       " ('H', 0.003232505721602191),\n",
       " ('N', 0.0038635502388189246),\n",
       " ('T', 0.004283755361816068),\n",
       " ('Y', 0.0046610051719975995),\n",
       " ('Q', 0.004865255733207527),\n",
       " ('K', 0.004946778056355858),\n",
       " ('E', 0.004995800545674859),\n",
       " ('A', 0.00595359012879638),\n",
       " ('I', 0.006942230921520069),\n",
       " ('R', 0.008046217619625016),\n",
       " ('S', 0.008577632074862129),\n",
       " ('L', 0.00890728613602228),\n",
       " ('C', 0.009152544414049193),\n",
       " ('V', 0.010110287603388762),\n",
       " ('G', 0.01240817873146247),\n",
       " ('*', 0.01353179671946442)]"
      ]
     },
     "execution_count": 1249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_correlated_aa_from_neighbours(\n",
    "    species_codon_df, distance_matrix, ratio_columns, species_taxid=2336, max_distance=threshold_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build graph of nearby species "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1197,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_graph(species_codon_df, distance_matrix, max_distance):\n",
    "    graph = nx.Graph()\n",
    "    \n",
    "    for ix in range(len(species_codon_df)):\n",
    "        if (ix + 1) % 200 == 0:\n",
    "            logger.info(f'{ix + 1} / {len(species_codon_df)}')\n",
    "            \n",
    "        species_series = species_codon_df.loc[ix]\n",
    "        \n",
    "        species_taxid = species_series['species_taxid']\n",
    "        if species_taxid not in graph.nodes:\n",
    "            add_node(graph, species_series)\n",
    "\n",
    "        nearby_ix = [\n",
    "            i for i, v in enumerate(distance_matrix[ix])\n",
    "            if i != ix and v <= max_distance\n",
    "        ]\n",
    "        \n",
    "        for other_ix in nearby_ix:\n",
    "            other_species_series = species_codon_df.loc[other_ix]\n",
    "            other_species_taxid = other_species_series['species_taxid']\n",
    "            \n",
    "            if graph.has_edge(species_taxid, other_species_taxid):\n",
    "                continue\n",
    "            \n",
    "            if other_species_taxid not in graph.nodes:\n",
    "                add_node(graph, other_species_series)\n",
    "                \n",
    "            graph.add_edge(species_taxid, other_species_taxid, weight=distance_matrix[ix, other_ix])\n",
    "\n",
    "    return graph\n",
    "\n",
    "\n",
    "def add_node(graph, species_series):\n",
    "    attributes = {\n",
    "        'species': species_series['species'],\n",
    "        'growth_tmp': species_series['growth_tmp'],\n",
    "        'genus': species_series['genus'], \n",
    "        'family': species_series['family'], \n",
    "        'order': species_series['order'],\n",
    "        'class': species_series['class'], \n",
    "        'phylum': species_series['phylum'], \n",
    "        'superkingdom': species_series['superkingdom'],\n",
    "    }\n",
    "    graph.add_node(species_series['species_taxid'], **attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1198,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-11-11 21:05:56,054 (INFO) 100 / 2680\n",
      "2020-11-11 21:05:58,833 (INFO) 200 / 2680\n",
      "2020-11-11 21:06:00,807 (INFO) 300 / 2680\n",
      "2020-11-11 21:06:04,204 (INFO) 400 / 2680\n",
      "2020-11-11 21:06:06,586 (INFO) 500 / 2680\n",
      "2020-11-11 21:06:09,686 (INFO) 600 / 2680\n",
      "2020-11-11 21:06:10,670 (INFO) 700 / 2680\n",
      "2020-11-11 21:06:12,699 (INFO) 800 / 2680\n",
      "2020-11-11 21:06:15,298 (INFO) 900 / 2680\n",
      "2020-11-11 21:06:18,191 (INFO) 1000 / 2680\n",
      "2020-11-11 21:06:20,433 (INFO) 1100 / 2680\n",
      "2020-11-11 21:06:22,573 (INFO) 1200 / 2680\n",
      "2020-11-11 21:06:25,129 (INFO) 1300 / 2680\n",
      "2020-11-11 21:06:27,699 (INFO) 1400 / 2680\n",
      "2020-11-11 21:06:29,994 (INFO) 1500 / 2680\n",
      "2020-11-11 21:06:32,567 (INFO) 1600 / 2680\n",
      "2020-11-11 21:06:34,589 (INFO) 1700 / 2680\n",
      "2020-11-11 21:06:36,784 (INFO) 1800 / 2680\n",
      "2020-11-11 21:06:38,961 (INFO) 1900 / 2680\n",
      "2020-11-11 21:06:41,607 (INFO) 2000 / 2680\n",
      "2020-11-11 21:06:43,870 (INFO) 2100 / 2680\n",
      "2020-11-11 21:06:46,292 (INFO) 2200 / 2680\n",
      "2020-11-11 21:06:48,587 (INFO) 2300 / 2680\n",
      "2020-11-11 21:06:50,826 (INFO) 2400 / 2680\n",
      "2020-11-11 21:06:53,132 (INFO) 2500 / 2680\n",
      "2020-11-11 21:06:55,161 (INFO) 2600 / 2680\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 218 ms, total: 1min 3s\n",
      "Wall time: 1min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "graph = build_graph(species_codon_df, distance_matrix, max_distance=threshold_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 1207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.number_connected_components(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{200, 824}\n",
      "{45363, 53558}\n"
     ]
    }
   ],
   "source": [
    "for component in nx.connected_components(graph):\n",
    "    if len(component) == 2:\n",
    "        print(component) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1518,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_temperature_from_neighbours_with_different_phylum(species_taxid):\n",
    "    current_node = graph.nodes[species_taxid]\n",
    "    current_phylum = current_node['phylum']\n",
    "    tmps = []\n",
    "    for key in graph.neighbors(species_taxid):\n",
    "        node = graph.nodes[key]\n",
    "        if key != species_taxid and node['phylum'] != current_phylum:\n",
    "            temp = node['growth_tmp']\n",
    "            tmps.append(temp)\n",
    "            \n",
    "    if len(tmps) == 0:\n",
    "        prediction = None\n",
    "    else:\n",
    "        prediction = np.mean(tmps)\n",
    "\n",
    "    return current_node['growth_tmp'], prediction\n",
    "\n",
    "\n",
    "def predict_temperature_from_neighbours(species_taxid):\n",
    "    current_node = graph.nodes[species_taxid]\n",
    "    tmps = []\n",
    "    for key in graph.neighbors(species_taxid):\n",
    "        node = graph.nodes[key]\n",
    "        if key != species_taxid:\n",
    "            temp = node['growth_tmp']\n",
    "            tmps.append(temp)\n",
    "            \n",
    "    if len(tmps) == 0:\n",
    "        prediction = None\n",
    "    else:\n",
    "        prediction = np.mean(tmps)\n",
    "\n",
    "    return current_node['growth_tmp'], prediction\n",
    "\n",
    "\n",
    "def compute_mae_from_neighbour_predictions(species_taxids, control_for_phylum=True):\n",
    "    actuals = []\n",
    "    predictions = []\n",
    "\n",
    "    for i, species_taxid in enumerate(species_taxids):\n",
    "        if control_for_phylum:\n",
    "            actual, prediction = predict_temperature_from_neighbours_with_different_phylum(species_taxid)\n",
    "        else:\n",
    "            actual, prediction = predict_temperature_from_neighbours(species_taxid)\n",
    "        \n",
    "        if prediction is None:\n",
    "            continue\n",
    "        \n",
    "        actuals.append(actual)\n",
    "        predictions.append(prediction)\n",
    "        \n",
    "    return mae(np.mean(actuals), np.mean(predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80.0, 78.725)"
      ]
     },
     "execution_count": 1519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_temperature_from_neighbours_with_different_phylum(2336)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.0, 33.42583333333334)"
      ]
     },
     "execution_count": 1520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_temperature_from_neighbours_with_different_phylum(120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1521,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31.0, 31.8156976744186)"
      ]
     },
     "execution_count": 1521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_temperature_from_neighbours_with_different_phylum(29405)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22799978735923787"
      ]
     },
     "execution_count": 1522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_mae_from_neighbour_predictions(species_codon_df['species_taxid'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0273254520904018"
      ]
     },
     "execution_count": 1523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_mae_from_neighbour_predictions(species_codon_df['species_taxid'].values, control_for_phylum=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-77.66990291262135"
      ]
     },
     "execution_count": 1525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * (0.23 - 1.03) / 1.03"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What genes are driving the overall codon bias? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1342,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance_to_species_ratios(species_codon_df, ratio_columns, species_taxid, assembly_accession):\n",
    "    query = \"\"\"\n",
    "    select metadata_json, sequence from sequences\n",
    "    where assembly_accession = ? and sequence_type = 'CDS'\n",
    "    \"\"\"\n",
    "    df = pd.read_sql(query, engine, params=(assembly_accession,))\n",
    "    \n",
    "    coding_sequences = df['sequence'].values\n",
    "    descriptions = df['metadata_json'].values\n",
    "    \n",
    "    p = species_codon_df[\n",
    "        species_codon_df['species_taxid'] == species_taxid\n",
    "    ].iloc[0][ratio_columns].values.astype(float)\n",
    "    \n",
    "    aa_indices = get_aa_indices()\n",
    "    \n",
    "    distances = []\n",
    "    metadata = []\n",
    "    for i, sequence in enumerate(coding_sequences):\n",
    "        q = compute_codon_ratios(sequence, ratio_columns)\n",
    "        dist = compute_distance_to_target(p, q, aa_indices)\n",
    "        distances.append(dist)\n",
    "        metadata.append(descriptions[i]) \n",
    "        \n",
    "    return np.array(distances), np.array(metadata)\n",
    "\n",
    "\n",
    "def compute_distance_to_target(x, y, aa_indices):\n",
    "    distances = []\n",
    "    for ix in aa_indices:\n",
    "        p = x[ix]\n",
    "        q = y[ix]\n",
    "        \n",
    "        d = jensen_shannon_divergence(p, q)\n",
    "        \n",
    "        distances.append(d)\n",
    "        \n",
    "    return np.mean(distances)\n",
    "\n",
    "        \n",
    "def compute_codon_ratios(sequence, ratio_columns):\n",
    "    codon_count = collections.defaultdict(int)\n",
    "    assert len(sequence) % 3 == 0\n",
    "    for pos in range(0, len(sequence), 3):\n",
    "        codon = sequence[pos:pos+3]\n",
    "        if codon not in CODON_REDUNDANCY:\n",
    "            # Ignore ambiguous letters (e.g. N, etc)\n",
    "            continue\n",
    "\n",
    "        codon_count[codon] += 1\n",
    "        \n",
    "    codon_ratios = np.zeros((62,))\n",
    "    \n",
    "    ordered_codons = [c.replace('_ratio', '') for c in ratio_columns]\n",
    "    \n",
    "    processed_codons = set()\n",
    "    for codon in sorted(CODON_REDUNDANCY.keys()):\n",
    "        if codon in processed_codons:\n",
    "            continue\n",
    "        elif len(CODON_REDUNDANCY[codon]) <= 1:\n",
    "            processed_codons.add(codon)\n",
    "            continue\n",
    "        \n",
    "        synonymous_codons = CODON_REDUNDANCY[codon]\n",
    "        \n",
    "        aa_sum = np.sum([codon_count[cod] for cod, _ in synonymous_codons])\n",
    "        \n",
    "        for i, (c, _) in enumerate(synonymous_codons):\n",
    "            processed_codons.add(c)\n",
    "            \n",
    "            ix = ordered_codons.index(c)\n",
    "            \n",
    "            if aa_sum > 0:\n",
    "                codon_ratios[ix] = codon_count[c] / aa_sum\n",
    "            else:\n",
    "                codon_ratios[ix] = 1 / len(synonymous_codons)\n",
    "            \n",
    "    return codon_ratios\n",
    "\n",
    "\n",
    "def get_aa_indices():\n",
    "    processed_codons = set()\n",
    "    indices = []\n",
    "    i = 0\n",
    "    for codon in sorted(CODON_REDUNDANCY.keys()):\n",
    "        if codon in processed_codons:\n",
    "            continue\n",
    "\n",
    "        elif len(CODON_REDUNDANCY[codon]) <= 1:\n",
    "            processed_codons.add(codon)\n",
    "            continue\n",
    "\n",
    "        row = []\n",
    "        synonymous_codons = CODON_REDUNDANCY[codon]\n",
    "        for c, _ in synonymous_codons:\n",
    "            processed_codons.add(c)\n",
    "            row.append(i)\n",
    "            i += 1\n",
    "\n",
    "        indices.append(row)\n",
    "\n",
    "    return indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assembly_accession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCA_000789255.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  assembly_accession\n",
       "0    GCA_000789255.1"
      ]
     },
     "execution_count": 1351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\n",
    "    'select assembly_accession from assembly_source where species_taxid = 565033',\n",
    "    engine\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1343,
   "metadata": {},
   "outputs": [],
   "source": [
    "species_taxid = 2336\n",
    "assembly_accession = 'GCA_000008545.1'\n",
    "distances, metadata = distance_to_species_ratios(species_codon_df, ratio_columns, species_taxid, assembly_accession)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1359,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proteins_following_codon_bias(species_codon_df, ratio_columns, species_taxid, assembly_accession):\n",
    "    distances, metadata = distance_to_species_ratios(\n",
    "        species_codon_df, ratio_columns, species_taxid, assembly_accession)\n",
    "    sort_ix = np.argsort(distances)\n",
    "    thres = np.mean(distances) - 1.1 * np.std(distances)\n",
    "    \n",
    "    _, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    ax.hist(distances, bins=50);\n",
    "    ax.axvline(thres, color='red');\n",
    "    \n",
    "    return [json.loads(metadata[ix])['protein'] for ix in sort_ix if distances[ix] <= thres]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1360,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DNA-directed RNA polymerase, beta subunit',\n",
       " 'comE protein, putative',\n",
       " 'alpha-mannosidase, putative',\n",
       " 'conserved hypothetical protein',\n",
       " 'alpha-mannosidase-related protein',\n",
       " 'hypothetical protein',\n",
       " 'conserved hypothetical protein',\n",
       " 'hypothetical protein',\n",
       " 'sensor histidine kinase',\n",
       " 'conserved hypothetical protein',\n",
       " 'iron(II) transport protein B',\n",
       " 'DNA-directed DNA polymerase I',\n",
       " 'conserved hypothetical protein',\n",
       " 'DNA gyrase, subunit A',\n",
       " 'cation-transporting ATPase, P-type',\n",
       " 'lipopolysaccharide biosynthesis protein',\n",
       " '(R)-2-hydroxyglutaryl-CoA dehydratase activator-related protein',\n",
       " 'oligopeptide ABC transporter, periplasmic oligopeptide-binding protein',\n",
       " 'DNA mismatch repair protein',\n",
       " 'conserved hypothetical protein',\n",
       " 'pyrimidine-nucleoside phosphorylase',\n",
       " 'pleD-related protein',\n",
       " 'anthranilate synthase component II',\n",
       " 'alpha-amylase',\n",
       " 'conserved hypothetical protein',\n",
       " '5-methyltetrahydrofolate S-homocysteine methyltransferase',\n",
       " 'beta-glucosidase',\n",
       " 'beta-D-galactosidase',\n",
       " 'single stranded DNA-specific exonuclease, putative',\n",
       " 'DNA ligase',\n",
       " 'conserved hypothetical protein',\n",
       " 'ribonucleotide reductase, B12-dependent',\n",
       " 'cellobiose-phosphorylase',\n",
       " '(p)ppGpp synthetase',\n",
       " 'conserved hypothetical protein',\n",
       " 'flagellar hook-associated protein 1',\n",
       " 'preprotein translocase SecA subunit',\n",
       " 'isoleucyl-tRNA synthetase',\n",
       " 'alpha-xylosidase',\n",
       " 'UDP-N-acetylglucosamine 1-carboxyvinyltransferase',\n",
       " 'pyruvate kinase',\n",
       " 'conserved hypothetical protein',\n",
       " '6-phosphogluconate dehydrogenase, decarboxylating',\n",
       " 'hypothetical protein',\n",
       " 'conserved hypothetical protein',\n",
       " 'alcohol dehydrogenase, zinc-containing',\n",
       " 'transcription termination factor Rho',\n",
       " 'DNA topoisomerase',\n",
       " \"DNA-directed RNA polymerase, beta' subunit\",\n",
       " 'DNA helicase, putative',\n",
       " 'oxidoreductase, putative',\n",
       " 'conserved hypothetical protein',\n",
       " 'conserved hypothetical protein, GGDEF domain',\n",
       " 'conserved hypothetical protein',\n",
       " 'ABC transporter, ATP-binding protein',\n",
       " 'electron transport complex protein, putative',\n",
       " 'propionyl-CoA carboxylase, beta subunit',\n",
       " 'ATP-dependent protease LA',\n",
       " 'penicillin-binding protein, class 1A',\n",
       " 'extracellular polysaccharide biosynthesis-related protein',\n",
       " 'lipopolysaccharide biosynthesis protein, putative',\n",
       " 'glycerol kinase',\n",
       " '5-methyltetrahydropteroyltriglutamate--homocysteine methyltransferase',\n",
       " 'oligopeptide ABC transporter, periplasmic oligopeptide-binding protein',\n",
       " 'glycine dehydrogenase (decarboxylating) subunit 1',\n",
       " 'ABC transporter, ATP-binding protein',\n",
       " 'lepA protein',\n",
       " 'beta-galactosidase',\n",
       " 'conserved hypothetical protein',\n",
       " 'beta-galactosidase',\n",
       " 'glucosamine--fructose-6-phosphate aminotransferase, isomerizing',\n",
       " 'conserved hypothetical protein',\n",
       " 'cytoplasmic axial filament protein, putative',\n",
       " 'cyclomaltodextrinase, putative',\n",
       " 'conserved hypothetical protein',\n",
       " 'thiamin biosynthesis protein ThiI',\n",
       " 'conserved hypothetical protein',\n",
       " 'ATP-dependent Clp protease, ATPase subunit',\n",
       " 'fixC protein',\n",
       " '3-phosphoshikimate-1-carboxyvinyltransferase',\n",
       " 'conserved hypothetical protein',\n",
       " 'phenylalanyl-tRNA synthetase, alpha subunit',\n",
       " 'alpha-glucuronidase',\n",
       " 'protein-export membrane protein SecD, putative',\n",
       " 'hypothetical protein',\n",
       " 'ornithine decarboxylase',\n",
       " 'polynucleotide phosphorylase',\n",
       " 'dehydrogenase',\n",
       " 'oligopeptide ABC transporter, ATP-binding protein',\n",
       " 'ATP-dependent DNA helicase',\n",
       " 'leucyl-tRNA synthetase',\n",
       " 'NADH dehydrogenase, putative',\n",
       " 'conserved hypothetical protein',\n",
       " 'regulatory protein, putative',\n",
       " 'conserved hypothetical protein',\n",
       " 'conserved hypothetical protein']"
      ]
     },
     "execution_count": 1360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proteins_following_codon_bias(\n",
    "    species_codon_df, \n",
    "    ratio_columns, \n",
    "    species_taxid=2336, \n",
    "    assembly_accession='GCA_000008545.1',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1361,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ATP-dependent helicase',\n",
       " 'heterodisulfide reductase, subunit A',\n",
       " 'Phosphoribosylformylglycinamidine synthase, synthetase subunit',\n",
       " 'Long-chain-fatty-acid--CoA ligase',\n",
       " 'chorismate mutase/prephenate dehydratase',\n",
       " 'carboxypeptidase',\n",
       " 'Protein similar to polyadenylation specificity factor',\n",
       " 'Lead, cadmium, zinc and mercury transporting ATPase',\n",
       " 'hypothetical protein',\n",
       " 'Archaeal DNA polymerase I',\n",
       " 'ATP-dependent helicase',\n",
       " 'Long-chain-fatty-acid--CoA ligase',\n",
       " 'CAP family transcription factor',\n",
       " 'DNA recombination protein RmuC',\n",
       " 'CO dehydrogenase/acetyl-CoA synthase corrinoid activation protein',\n",
       " 'ATP-dependent RNA helicase, EIF-4A family',\n",
       " 'hypothetical protein',\n",
       " 'Radical SAM domain protein',\n",
       " 'transglutaminase-like protein',\n",
       " 'Cobyric acid synthase',\n",
       " \"DNA-directed RNA polymerase subunit A'\",\n",
       " 'Cell division control protein 6 (cdc6)',\n",
       " 'Phosphoribosylamine--glycine ligase',\n",
       " 'hypothetical protein',\n",
       " 'Radical SAM domain protein',\n",
       " 'pyridine nucleotide-disulfide oxidoreductase',\n",
       " 'Long-chain-fatty-acid--CoA ligase',\n",
       " 'indolepyruvate ferredoxin oxidoreductase alpha subunit',\n",
       " 'Helicase domain protein',\n",
       " 'Medium-chain-fatty-acid--CoA ligase',\n",
       " '[NiFe',\n",
       " 'Carbamoyl-phosphate synthase large chain',\n",
       " 'O-phosphoseryl-tRNA(Cys) synthetase',\n",
       " 'Long-chain-fatty-acid--CoA ligase',\n",
       " 'hypothetical protein',\n",
       " 'Thymidylate synthase',\n",
       " '4-hydroxyphenylacetate 3-monooxygenase',\n",
       " 'archaeosine tRNA-ribosyltransferase type 1',\n",
       " 'F420H2-quinone oxidoreductase, subunit M',\n",
       " 'hypothetical protein',\n",
       " 'acetyl-CoA synthetase',\n",
       " 'radical SAM domain protein, menaquinone biosynthesis',\n",
       " 'Acetolactate synthase large subunit',\n",
       " 'methyltransferase',\n",
       " 'CoB--CoM heterodisulfide reductase subunit A',\n",
       " 'O-succinylbenzoic acid--CoA ligase',\n",
       " 'DNA primase, phage related',\n",
       " '4-hydroxyphenylacetate 3-monooxygenase',\n",
       " 'Transcriptional regulatory protein',\n",
       " 'Cyclohex-1-ene-1-carboxyl-CoA hydratase, BadK',\n",
       " 'PilT protein domain protein',\n",
       " 'DNA mismatch repair protein MutS',\n",
       " 'hypothetical protein',\n",
       " 'hypothetical protein',\n",
       " 'S-layer domain protein',\n",
       " 'hypothetical protein',\n",
       " '2-oxoacid ferredoxin oxidoreductase, subunit beta',\n",
       " 'hypothetical protein',\n",
       " 'oligopeptide ABC transporter, permease protein',\n",
       " 'phytoene dehydrogenase',\n",
       " 'Isoleucyl-tRNA synthetase',\n",
       " 'Phosphoenolpyruvate synthase',\n",
       " 'methylviologen-reducing hydrogenase, subunit alpha (MvhA)',\n",
       " 'DNA double-strand break repair Rad50 ATPase',\n",
       " '3-hydroxyacyl-CoA dehydrogenase',\n",
       " 'Transporter',\n",
       " '[NiFe',\n",
       " 'Hydroxymethylglutaryl-CoA reductase',\n",
       " 'Amidophosphoribosyltransferase',\n",
       " 'hypothetical protein',\n",
       " 'Biotin carboxylase of acetyl-CoA carboxylase',\n",
       " 'hypothetical protein',\n",
       " 'Hydrogenase expression/formation protein',\n",
       " \"mRNA 3'-end processing factor\",\n",
       " 'bipolar DNA helicase',\n",
       " 'Histidyl-tRNA synthetase',\n",
       " 'hypothetical protein',\n",
       " 'F420H2-quinone oxidoreductase, subunit BC (fused)',\n",
       " 'DNA replication helicase protein MCM',\n",
       " 'carbon monoxide dehydrogenase, catalytic subunit (cooS)',\n",
       " 'cytochrome c family protein',\n",
       " 'glucosamine--fructose-6-phosphate aminotransferase,isomerizing',\n",
       " 'Adenylylsulfate reductase alpha-subunit',\n",
       " 'Trk system potassium uptake protein TrkA',\n",
       " 'acetyl-CoA acetyltransferase',\n",
       " 'heterodisulfide reductase iron-sulfur subunit D',\n",
       " 'Phage integrase',\n",
       " 'DNA topoisomerase VI subunit B',\n",
       " 'signal-transducing histidine kinase',\n",
       " 'Acetyl-CoA synthetase (ADP-forming) alpha and beta chains, putative',\n",
       " 'Deblocking aminopeptidase',\n",
       " 'Serine hydroxymethyltransferase',\n",
       " 'Phosphopantothenoylcysteine decarboxylase',\n",
       " 'Translation elongation factor 2',\n",
       " 'S-adenosylhomocysteine deaminase',\n",
       " 'ABC-type antimicrobial peptide transport system, permease component',\n",
       " 'hypothetical protein',\n",
       " 'Glutamyl-tRNA synthetase',\n",
       " 'Flagella-related protein FlaI',\n",
       " 'peptide ABC transporter, permease protein',\n",
       " 'radical SAM domain protein',\n",
       " 'Lysine 2,3-aminomutase',\n",
       " '2-oxoacid ferredoxin oxidoreductase, subunit alpha',\n",
       " 'radical SAM protein',\n",
       " 'Alanyl-tRNA synthetase',\n",
       " 'Radical SAM domain protein',\n",
       " 'Tyrosyl-tRNA synthetase',\n",
       " 'hypothetical protein',\n",
       " 'Na /H antiporter (napA-1)',\n",
       " 'Replication initiation protein',\n",
       " \"RNA 3'-terminal phosphate cyclase\",\n",
       " 'Septum site-determining protein MinD',\n",
       " 'indolepyruvate ferredoxin oxidoreductase alpha subunit',\n",
       " 'LSU ribosomal protein L3e (L3p)',\n",
       " 'Glutaconate CoA-transferase subunit B',\n",
       " 'cysteate synthase',\n",
       " 'Cell division control protein 6 (cdc6)',\n",
       " 'Porphobilinogen deaminase',\n",
       " 'hypothetical protein',\n",
       " 'UbiD family decarboxylase',\n",
       " 'Butyryl-CoA dehydrogenase',\n",
       " 'cytochrome c family protein',\n",
       " 'hypothetical protein',\n",
       " 'hypothetical protein',\n",
       " 'hypothetical protein',\n",
       " 'hypothetical protein',\n",
       " 'Replication factor C small subunit',\n",
       " 'hypothetical protein',\n",
       " 'Ferredoxin--nitrite reductase',\n",
       " 'archaeosine tRNA-ribosyltransferase type 2',\n",
       " 'Sodium/proline symporter',\n",
       " 'Phenylalanyl-tRNA synthetase beta chain',\n",
       " 'Archaeal DNA polymerase II large subunit',\n",
       " 'tRNA modifying enzyme',\n",
       " 'hypothetical protein',\n",
       " \"DNA-directed RNA polymerase subunit B''\",\n",
       " 'hypothetical protein',\n",
       " 'DNA gyrase subunit A',\n",
       " 'ATP-grasp fold domain-containing protein',\n",
       " 'Glucose-1-phosphate thymidylyltransferase',\n",
       " 'FAD/FMN-containing dehydrogenase',\n",
       " 'aldehyde:ferredoxin oxidoreductase',\n",
       " 'Adenosylhomocysteinase',\n",
       " 'Molybdopterin binding motif, CinA N-terminal domain',\n",
       " 'hypothetical protein',\n",
       " 'Flagellin FlaB1',\n",
       " 'ATP-dependent DNA ligase',\n",
       " 'tRNA-splicing ligase RtcB',\n",
       " 'Translation initiation factor 2',\n",
       " 'CBS-domain-containing protein',\n",
       " 'Methionyl-tRNA synthetase',\n",
       " 'Replication factor C large subunit',\n",
       " 'ski2-type helicase',\n",
       " 'Branched-chain amino acid transport system permease protein LivM',\n",
       " 'Putative zinc metalloprotease',\n",
       " 'Dihydrolipoamide dehydrogenase']"
      ]
     },
     "execution_count": 1361,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proteins_following_codon_bias(\n",
    "    species_codon_df, \n",
    "    ratio_columns, \n",
    "    species_taxid=565033, \n",
    "    assembly_accession='GCA_000789255.1',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most common protein within proteins following codon bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1618,
   "metadata": {},
   "outputs": [],
   "source": [
    "def protein_description_word_count(assembly_accession, below_threshold=True, count_type='document'):\n",
    "    if count_type not in ('document', 'term'):\n",
    "        raise ValueError('count type should be one of document or term')\n",
    "        \n",
    "    cds_codon_bias_folder = os.path.join(os.getcwd(), 'data/cds_codon_bias')\n",
    "    if below_threshold:\n",
    "        cds_codon_bias_folder = os.path.join(cds_codon_bias_folder, 'below_threshold')\n",
    "    else:\n",
    "        cds_codon_bias_folder = os.path.join(cds_codon_bias_folder, 'all')\n",
    "        \n",
    "    cds_codon_bias_path = os.path.join(cds_codon_bias_folder, f'{assembly_accession}_codon_bias.csv')\n",
    "    \n",
    "    black_list = [\n",
    "        '',\n",
    "        #'dna',\n",
    "        #'rna',\n",
    "        #'t-rna',\n",
    "        #'trna',\n",
    "        'protein',\n",
    "        'enzyme',\n",
    "        'subunit',\n",
    "        'domain',\n",
    "        'domain-containing',\n",
    "        'hypothetical',\n",
    "        'putative',\n",
    "        'probable',\n",
    "        'conserved',\n",
    "        'factor',\n",
    "        'large',\n",
    "        'small',\n",
    "        'family',\n",
    "        'superfamily',\n",
    "        'outer',\n",
    "        'cell',\n",
    "        'i',\n",
    "        'ii',\n",
    "        'iii',\n",
    "        'iv',\n",
    "        'v',\n",
    "        'vi',\n",
    "        'alpha',\n",
    "        'beta',\n",
    "        'gamma',\n",
    "        'type',\n",
    "        'and',\n",
    "        'or',\n",
    "        'of',\n",
    "        'class',\n",
    "        'assembly',\n",
    "        'acid',\n",
    "        'component',\n",
    "        'response',\n",
    "        '2x',\n",
    "        '1-2',\n",
    "    ]\n",
    "    black_list += [c for c in string.ascii_lowercase]\n",
    "    black_list += [c for c in string.digits]\n",
    "    black_list = set(black_list)\n",
    "    \n",
    "    df = pd.read_csv(cds_codon_bias_path)\n",
    "    descriptions = df['description'].values\n",
    "    \n",
    "    counts = collections.defaultdict(int)\n",
    "    for description in descriptions:\n",
    "        desc = str(description).replace(',', '').replace('.', '').replace('(', '').replace(')', '').strip()\n",
    "        parts_1 = desc.split(' ')\n",
    "        parts = []\n",
    "        for p in parts_1:\n",
    "            parts  += [v.lower() for v in p.split('/')]\n",
    "            \n",
    "        for word in parts:\n",
    "            if word in black_list:\n",
    "                continue\n",
    "                \n",
    "            if count_type == 'document':\n",
    "                counts[word] = 1\n",
    "            else:\n",
    "                counts[word] += 1\n",
    "    \n",
    "    return dict(counts)\n",
    "\n",
    "\n",
    "def compute_word_count(assembly_accessions, below_threshold=True, count_type='document', sorted_output=True):\n",
    "    if count_type not in ('document', 'term'):\n",
    "        raise ValueError('count type should be one of document or term')\n",
    "    \n",
    "    total_counts = {}\n",
    "    for i, assembly_accession in enumerate(assembly_accessions):\n",
    "        if (i+1) % 200 == 0:\n",
    "            print(f'{i+1} / {len(assembly_accessions)}')\n",
    "            \n",
    "        counts = protein_description_word_count(assembly_accession, below_threshold, count_type)\n",
    "        \n",
    "        for k, v in counts.items():\n",
    "            total_counts[k] = total_counts.get(k, 0) + counts[k]\n",
    "    \n",
    "    if sorted_output:\n",
    "        word_counts = [\n",
    "            (\n",
    "                k, \n",
    "                np.round(100 * v / len(assembly_accessions), 2)\n",
    "            ) \n",
    "\n",
    "            for k, v in total_counts.items()\n",
    "        ]\n",
    "\n",
    "        return sorted(word_counts, key=lambda t: t[1], reverse=True)\n",
    "    else:\n",
    "        return total_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1541,
   "metadata": {},
   "outputs": [],
   "source": [
    "assembly_accessions = codon_frequency_df['assembly_accession'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1513,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "CPU times: user 22.5 s, sys: 1.67 s, total: 24.1 s\n",
      "Wall time: 24.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "word_counts = compute_word_count(assembly_accessions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23178"
      ]
     },
     "execution_count": 1514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1515,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('synthase', 95.86),\n",
       " ('helicase', 92.14),\n",
       " ('kinase', 91.8),\n",
       " ('dehydrogenase', 90.07),\n",
       " ('polymerase', 88.79),\n",
       " ('transporter', 88.73),\n",
       " ('abc', 87.14),\n",
       " ('ligase', 86.72),\n",
       " ('atpase', 86.11),\n",
       " ('reductase', 85.89),\n",
       " ('synthetase', 85.5),\n",
       " ('atp-dependent', 85.47),\n",
       " ('oxidoreductase', 80.65),\n",
       " ('regulator', 74.83),\n",
       " ('atp-binding', 74.71),\n",
       " ('histidine', 74.53),\n",
       " ('hydrolase', 72.82),\n",
       " ('permease', 70.29),\n",
       " ('peptidase', 70.26),\n",
       " ('repair', 67.89),\n",
       " ('transcriptional', 66.76),\n",
       " ('system', 66.58),\n",
       " ('protease', 66.21),\n",
       " ('membrane', 64.2),\n",
       " ('oxidase', 64.14),\n",
       " ('biosynthesis', 63.86),\n",
       " ('sensor', 62.74),\n",
       " ('topoisomerase', 61.4),\n",
       " ('transferase', 59.99),\n",
       " ('aminotransferase', 59.78),\n",
       " ('serine', 58.96),\n",
       " ('excinuclease', 58.56),\n",
       " ('penicillin-binding', 57.1),\n",
       " ('mismatch', 54.24),\n",
       " ('carboxylase', 52.77),\n",
       " ('pyruvate', 52.71),\n",
       " ('dehydratase', 52.65),\n",
       " ('transport', 51.83),\n",
       " ('glycosyl', 49.76),\n",
       " ('decarboxylase', 49.39),\n",
       " ('glutamate', 49.12),\n",
       " ('methyltransferase', 49.06),\n",
       " ('bifunctional', 48.45),\n",
       " ('cyclase', 47.96),\n",
       " ('phosphate', 47.65),\n",
       " ('division', 47.62),\n",
       " ('amino', 47.32),\n",
       " ('exonuclease', 47.2),\n",
       " ('aminopeptidase', 46.07),\n",
       " ('ribonuclease', 45.95),\n",
       " ('p-type', 45.58),\n",
       " ('cytochrome', 45.19),\n",
       " ('translocase', 44.42),\n",
       " ('signal', 42.9),\n",
       " ('peptide', 42.72),\n",
       " ('threonine', 41.8),\n",
       " ('secretion', 41.77),\n",
       " ('multidrug', 41.71),\n",
       " ('gyrase', 41.62),\n",
       " ('efflux', 41.56),\n",
       " ('coupling', 41.41),\n",
       " ('chain', 40.92),\n",
       " ('muts', 40.89),\n",
       " ('transcription-repair', 40.65),\n",
       " ('diguanylate', 39.55),\n",
       " ('aldehyde', 38.79),\n",
       " ('phosphatase', 38.7),\n",
       " ('ftsk', 38.3),\n",
       " ('phosphoribosylformylglycinamidine', 38.3),\n",
       " ('glycosyltransferase', 37.9),\n",
       " ('resistance', 37.42),\n",
       " ('phosphorylase', 37.32),\n",
       " ('acyl-coa', 37.23),\n",
       " ('lyase', 37.17),\n",
       " ('pas', 37.02),\n",
       " ('box', 36.47),\n",
       " ('chemotaxis', 36.44),\n",
       " ('phosphoenolpyruvate', 36.23),\n",
       " ('antiporter', 36.05),\n",
       " ('iron', 35.65),\n",
       " ('radical', 35.13),\n",
       " ('receptor', 34.92),\n",
       " ('sugar', 34.92),\n",
       " ('chaperone', 34.8),\n",
       " ('hydratase', 34.7),\n",
       " ('sam', 34.61),\n",
       " ('ferredoxin', 34.03),\n",
       " ('glycine', 33.91),\n",
       " ('glycogen', 33.82),\n",
       " ('isomerase', 33.76),\n",
       " ('acetyl-coa', 33.76),\n",
       " ('endonuclease', 33.76),\n",
       " ('dead', 33.39),\n",
       " ('mutase', 33.21),\n",
       " ('phosphodiesterase', 32.54),\n",
       " ('chromosome', 32.36),\n",
       " ('binding', 31.96),\n",
       " ('smc', 31.81),\n",
       " ('aspartate', 31.54),\n",
       " ('substrate-binding', 30.9)]"
      ]
     },
     "execution_count": 1515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1516,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "ax.hist([int(np.round(t[1], 0)) for t in word_counts], bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1537,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "CPU times: user 2min 16s, sys: 14.9 s, total: 2min 31s\n",
      "Wall time: 2min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "word_counts = compute_word_count(assembly_accessions, below_threshold=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('synthase', 99.88),\n",
       " ('kinase', 99.88),\n",
       " ('ribosomal', 99.88),\n",
       " ('atp', 99.76),\n",
       " ('methyltransferase', 99.7),\n",
       " ('polymerase', 99.7),\n",
       " ('dehydrogenase', 99.57),\n",
       " ('isomerase', 99.54),\n",
       " ('transporter', 99.51),\n",
       " ('phosphoribosyltransferase', 99.51),\n",
       " ('ribonuclease', 99.45),\n",
       " ('abc', 99.42),\n",
       " ('initiation', 99.42),\n",
       " ('elongation', 99.3),\n",
       " ('signal', 99.24),\n",
       " ('transcription', 99.21),\n",
       " ('deaminase', 99.21),\n",
       " ('translation', 99.18),\n",
       " ('helicase', 99.12),\n",
       " ('regulator', 99.12),\n",
       " ('reductase', 99.02),\n",
       " ('system', 98.96),\n",
       " ('atp-binding', 98.93),\n",
       " ('dna-binding', 98.93),\n",
       " ('hydrolase', 98.75),\n",
       " ('transcriptional', 98.69),\n",
       " ('chain', 98.6),\n",
       " ('atpase', 98.48),\n",
       " ('transferase', 98.42),\n",
       " ('synthetase', 98.32),\n",
       " ('adenylate', 98.29),\n",
       " ('serine', 98.23),\n",
       " ('rrna', 98.2),\n",
       " ('thioredoxin', 98.2),\n",
       " ('adenylyltransferase', 98.11),\n",
       " ('phosphate', 98.05),\n",
       " ('dna-directed', 98.02),\n",
       " ('acetyltransferase', 97.99),\n",
       " ('endonuclease', 97.99),\n",
       " ('biosynthesis', 97.9),\n",
       " ('peptide', 97.9),\n",
       " ('division', 97.87),\n",
       " ('pyruvate', 97.78),\n",
       " ('oxidoreductase', 97.71),\n",
       " ('atp-dependent', 97.65),\n",
       " ('aldolase', 97.65),\n",
       " ('ligase', 97.62),\n",
       " ('translocase', 97.56),\n",
       " ('amidotransferase', 97.5),\n",
       " ('peptidase', 97.26),\n",
       " ('repair', 97.2),\n",
       " ('protease', 97.2),\n",
       " ('aminopeptidase', 97.2),\n",
       " ('phosphatase', 97.17),\n",
       " ('decarboxylase', 97.11),\n",
       " ('mutase', 97.11),\n",
       " ('oxidase', 96.92),\n",
       " ('dehydratase', 96.92),\n",
       " ('release', 96.89),\n",
       " ('phosphorylase', 96.86),\n",
       " ('aminotransferase', 96.86),\n",
       " ('cyclohydrolase', 96.83),\n",
       " ('lyase', 96.8),\n",
       " ('phosphoglycerate', 96.77),\n",
       " ('permease', 96.68),\n",
       " ('carrier', 96.68),\n",
       " ('membrane', 96.65),\n",
       " ('gyrase', 96.56),\n",
       " ('glutamate', 96.53),\n",
       " ('chaperone', 96.5),\n",
       " ('aspartate', 96.47),\n",
       " ('amino', 96.4),\n",
       " ('replication', 96.37),\n",
       " ('methionine', 96.31),\n",
       " ('riboflavin', 96.19),\n",
       " ('histidine', 96.16),\n",
       " ('pseudouridine', 96.13),\n",
       " ('cysteine', 96.1),\n",
       " ('hydroxymethyltransferase', 96.04),\n",
       " ('ribosome', 96.04),\n",
       " ('recognition', 95.95),\n",
       " ('holliday', 95.83),\n",
       " ('formyltransferase', 95.8),\n",
       " ('carboxylase', 95.73),\n",
       " ('pyrophosphatase', 95.67),\n",
       " ('thymidylate', 95.64),\n",
       " ('adenine', 95.61),\n",
       " ('sugar', 95.61),\n",
       " ('nucleoside', 95.58),\n",
       " ('gmp', 95.46),\n",
       " ('carbamoyltransferase', 95.46),\n",
       " ('channel', 95.43),\n",
       " ('delta', 95.37),\n",
       " ('threonine', 95.28),\n",
       " ('epimerase', 95.16),\n",
       " ('glycosylase', 95.09),\n",
       " ('deacetylase', 95.06),\n",
       " ('adenylosuccinate', 95.03),\n",
       " ('junction', 95.0),\n",
       " ('transport', 94.97)]"
      ]
     },
     "execution_count": 1538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "ax.hist([int(np.round(t[1], 0)) for t in word_counts], bins=100, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1682,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_word_scores(assembly_accessions):\n",
    "    top_term_freq = compute_word_count(\n",
    "        assembly_accessions,\n",
    "        below_threshold=True,\n",
    "        count_type='term',\n",
    "        sorted_output=False,\n",
    "    )\n",
    "    all_term_freq = compute_word_count(\n",
    "        assembly_accessions,\n",
    "        below_threshold=False,\n",
    "        count_type='term',\n",
    "        sorted_output=False,\n",
    "    )\n",
    "    top_document_freq = compute_word_count(\n",
    "        assembly_accessions,\n",
    "        below_threshold=True,\n",
    "        count_type='document',\n",
    "        sorted_output=False,\n",
    "    )\n",
    "    all_document_freq = compute_word_count(\n",
    "        assembly_accessions,\n",
    "        below_threshold=False,\n",
    "        count_type='document',\n",
    "        sorted_output=False,\n",
    "    )\n",
    "    all_words = sorted(all_document_freq.keys())\n",
    "    \n",
    "    word_scores = []\n",
    "    for word in all_words:\n",
    "        term_freq = top_term_freq.get(word, 0)\n",
    "        document_freq = top_document_freq.get(word, 0)\n",
    "        \n",
    "        if term_freq < 100:\n",
    "            continue\n",
    "        elif document_freq < 10:\n",
    "            continue\n",
    "            \n",
    "        term_score = term_freq / all_term_freq[word]\n",
    "        document_score = document_freq / all_document_freq[word]\n",
    "        score = (term_score + document_score) / 2\n",
    "        \n",
    "        word_scores.append((\n",
    "            word, score, term_score, document_score\n",
    "        ))\n",
    "        \n",
    "    return (\n",
    "        sorted(word_scores, key=lambda t: t[1], reverse=True), \n",
    "        top_term_freq, \n",
    "        all_term_freq,\n",
    "        top_document_freq,\n",
    "        all_document_freq,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1683,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "1,376\n",
      "CPU times: user 5min 22s, sys: 32.3 s, total: 5min 55s\n",
      "Wall time: 6min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "(\n",
    "    word_scores,\n",
    "    top_term_freq, \n",
    "    all_term_freq,\n",
    "    top_document_freq,\n",
    "    all_document_freq,\n",
    ") = compute_word_scores(assembly_accessions)\n",
    "print(f'{len(word_scores):,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1688,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_scores_2 = sorted(word_scores, key=lambda t: t[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1689,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('helicase-exonuclease', 80.69, 0.9, 0.88, 0.92),\n",
       " ('addab', 78.78, 0.89, 0.86, 0.91),\n",
       " ('adda', 70.19, 0.84, 0.84, 0.84),\n",
       " ('dnae', 65.57, 0.81, 0.8, 0.82),\n",
       " ('addb', 63.92, 0.8, 0.8, 0.8),\n",
       " ('spra', 61.17, 0.78, 0.78, 0.79),\n",
       " ('modules', 53.96, 0.74, 0.64, 0.84),\n",
       " ('polc', 54.84, 0.74, 0.71, 0.77),\n",
       " ('non-ribosomal', 54.3, 0.74, 0.69, 0.78),\n",
       " ('adenylation', 52.51, 0.73, 0.68, 0.77),\n",
       " ('duf490', 48.95, 0.7, 0.69, 0.71),\n",
       " ('alpha-2-macroglobulin', 46.74, 0.68, 0.67, 0.7),\n",
       " ('cobn', 46.35, 0.68, 0.66, 0.71),\n",
       " ('lhr', 45.14, 0.67, 0.65, 0.69),\n",
       " ('transcription-repair', 43.19, 0.66, 0.65, 0.66),\n",
       " ('transcription-repair-coupling', 41.6, 0.65, 0.63, 0.66),\n",
       " ('ca2+-transporting', 40.96, 0.64, 0.62, 0.66),\n",
       " ('polc-type', 40.14, 0.64, 0.58, 0.69),\n",
       " ('hrpa', 40.15, 0.63, 0.61, 0.66),\n",
       " ('alpha-mannosidase', 38.35, 0.62, 0.55, 0.7),\n",
       " ('nonribosomal', 38.69, 0.62, 0.62, 0.63),\n",
       " ('helicase', 26.48, 0.62, 0.28, 0.95),\n",
       " ('coupling', 37.48, 0.61, 0.57, 0.65),\n",
       " ('s-box-containing', 30.21, 0.6, 0.35, 0.86),\n",
       " ('deadenyltransferase', 36.15, 0.6, 0.6, 0.6),\n",
       " ('bara', 35.56, 0.6, 0.55, 0.65),\n",
       " ('glutamine-synthetase', 35.26, 0.59, 0.59, 0.59),\n",
       " ('s-dna-t', 34.86, 0.59, 0.55, 0.64),\n",
       " ('maltooligosyl', 34.75, 0.59, 0.55, 0.63),\n",
       " ('dna', 18.3, 0.59, 0.19, 0.99),\n",
       " ('tigr02099', 34.18, 0.58, 0.58, 0.58),\n",
       " ('pullulanase', 33.85, 0.58, 0.55, 0.62),\n",
       " ('nad-glutamate', 33.9, 0.58, 0.58, 0.58),\n",
       " ('arabinosyltransferase', 33.39, 0.58, 0.52, 0.64),\n",
       " ('5-carboxymethylaminomethyl-2-thiouridine34', 33.38, 0.58, 0.58, 0.58),\n",
       " ('uvra', 33.26, 0.58, 0.55, 0.6),\n",
       " ('glutamate-ammonia-ligase', 33.27, 0.58, 0.57, 0.58),\n",
       " ('uridylyl-removing', 32.56, 0.57, 0.57, 0.57),\n",
       " ('multi-sensor', 31.28, 0.57, 0.46, 0.68),\n",
       " ('alpha-n-acetylglucosaminidase', 32.22, 0.57, 0.52, 0.62),\n",
       " ('atp-dependent', 19.91, 0.57, 0.22, 0.92),\n",
       " ('afsr', 30.31, 0.57, 0.43, 0.7),\n",
       " ('cu+-exporting', 31.83, 0.57, 0.53, 0.6),\n",
       " ('puta', 31.34, 0.56, 0.54, 0.58),\n",
       " ('mnmd', 31.23, 0.56, 0.56, 0.56),\n",
       " ('ftsk', 30.61, 0.56, 0.49, 0.63),\n",
       " ('mcm', 29.92, 0.55, 0.5, 0.6),\n",
       " ('tonb-dependent', 23.87, 0.55, 0.3, 0.8),\n",
       " ('t9ss', 21.07, 0.55, 0.25, 0.85),\n",
       " ('sbcc', 29.86, 0.55, 0.52, 0.57),\n",
       " ('calcium-translocating', 29.63, 0.55, 0.5, 0.59),\n",
       " ('mukb', 29.48, 0.54, 0.54, 0.55),\n",
       " ('excinuclease', 26.13, 0.54, 0.36, 0.73),\n",
       " ('gramicidin', 28.9, 0.54, 0.49, 0.59),\n",
       " ('smc', 28.27, 0.54, 0.46, 0.61),\n",
       " ('sensors', 27.14, 0.53, 0.42, 0.64),\n",
       " ('muts', 27.33, 0.53, 0.44, 0.63),\n",
       " ('exodnase', 28.1, 0.53, 0.52, 0.54),\n",
       " ('pmca-type', 27.57, 0.53, 0.47, 0.59),\n",
       " ('restart', 27.76, 0.53, 0.49, 0.56),\n",
       " ('synthase', 6.97, 0.52, 0.07, 0.98),\n",
       " ('synthetase', 13.62, 0.52, 0.15, 0.89),\n",
       " ('magnesium-translocating', 27.16, 0.52, 0.5, 0.54),\n",
       " ('tamb', 27.06, 0.52, 0.52, 0.52),\n",
       " ('p-type', 24.0, 0.52, 0.35, 0.69),\n",
       " ('ligase', 11.73, 0.52, 0.13, 0.91),\n",
       " ('atpase', 11.23, 0.52, 0.12, 0.91),\n",
       " ('polymerase', 9.11, 0.52, 0.1, 0.94),\n",
       " ('hybrid', 25.27, 0.51, 0.41, 0.62),\n",
       " ('kinase', 8.8, 0.51, 0.09, 0.93),\n",
       " ('penicillin-binding', 21.54, 0.51, 0.3, 0.73),\n",
       " ('amphiphilic', 25.57, 0.51, 0.45, 0.57),\n",
       " ('oligosaccharyl', 25.67, 0.51, 0.48, 0.54),\n",
       " ('cu2+-exporting', 25.57, 0.51, 0.46, 0.55),\n",
       " ('alpha-l-rhamnosidase', 24.82, 0.51, 0.41, 0.6),\n",
       " ('acrd', 24.23, 0.5, 0.4, 0.61),\n",
       " ('uvrabc', 23.65, 0.5, 0.38, 0.63),\n",
       " ('exporter-1', 24.84, 0.5, 0.44, 0.56),\n",
       " ('csm1', 25.15, 0.5, 0.49, 0.52),\n",
       " ('recepter', 18.64, 0.5, 0.25, 0.75)]"
      ]
     },
     "execution_count": 1689,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\n",
    "    (t[0], np.round(100 * (t[2] * t[3]), 2), np.round(t[1], 2), np.round(t[2], 2), np.round(t[3], 2))\n",
    "    for t in word_scores_2\n",
    "    if t[1] >= 0.5\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1676,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "ax.hist([int(np.round(100 * t[2], 0)) for t in word_scores_2], bins=50, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1642,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n"
     ]
    }
   ],
   "source": [
    "top_term_freq = compute_word_count(\n",
    "    assembly_accessions,\n",
    "    below_threshold=True,\n",
    "    count_type='term',\n",
    "    sorted_output=False,\n",
    ")\n",
    "top_document_freq = compute_word_count(\n",
    "    assembly_accessions,\n",
    "    below_threshold=True,\n",
    "    count_type='document',\n",
    "    sorted_output=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1643,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_term_freq_sorted = sorted(list(top_term_freq.items()), key=lambda t: t[1], reverse=True)\n",
    "top_document_freq_sorted = sorted(list(top_document_freq.items()), key=lambda t: t[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1644,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6172"
      ]
     },
     "execution_count": 1644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([t[1] for t in top_term_freq_sorted if t[1] >= 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7767"
      ]
     },
     "execution_count": 1645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([t[1] for t in top_document_freq_sorted if t[1] > 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve most common initial protein name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1692,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_most_common_protein_from_words(assembly_accessions, words):\n",
    "    cds_codon_bias_folder = os.path.join(os.getcwd(), 'data/cds_codon_bias/below_threshold')\n",
    "\n",
    "    counts = {}\n",
    "    for ix, assembly_accession in enumerate(assembly_accessions):\n",
    "        if (ix + 1) % 200 == 0:\n",
    "            print(f'{ix + 1} / {len(assembly_accessions)}')\n",
    "        \n",
    "        cds_codon_bias_path = os.path.join(cds_codon_bias_folder, f'{assembly_accession}_codon_bias.csv')\n",
    "\n",
    "        df = pd.read_csv(cds_codon_bias_path)\n",
    "        descriptions = df['description'].values\n",
    "\n",
    "        for description in descriptions:\n",
    "            protein = str(description)\n",
    "            desc = str(description).replace(',', '').replace('.', '').replace('(', '').replace(')', '').strip()\n",
    "            parts_1 = desc.split(' ')\n",
    "            parts = []\n",
    "            for p in parts_1:\n",
    "                parts  += [v.lower() for v in p.split('/')]\n",
    "                \n",
    "            parts_set = set(parts)\n",
    "\n",
    "            for word in words:\n",
    "                if word not in parts_set:\n",
    "                    continue\n",
    "\n",
    "                if word not in counts:\n",
    "                    counts[word] = {}\n",
    "                    \n",
    "                if protein not in counts[word]:\n",
    "                    counts[word][protein] = 1\n",
    "                else:\n",
    "                    counts[word][protein] += 1\n",
    "                \n",
    "    word_to_most_common_protein = {}\n",
    "    for word in words:\n",
    "        if word not in counts:\n",
    "            continue\n",
    "        \n",
    "        l = sorted([(k, v) for k, v in counts[word].items()], key=lambda t: t[1])\n",
    "        word_to_most_common_protein[word] = l[-1][0]\n",
    "    \n",
    "    return word_to_most_common_protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1697,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_common_words = [t[0] for t in word_scores_2 if t[1] >= 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1693,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 / 3282\n",
      "400 / 3282\n",
      "600 / 3282\n",
      "800 / 3282\n",
      "1000 / 3282\n",
      "1200 / 3282\n",
      "1400 / 3282\n",
      "1600 / 3282\n",
      "1800 / 3282\n",
      "2000 / 3282\n",
      "2200 / 3282\n",
      "2400 / 3282\n",
      "2600 / 3282\n",
      "2800 / 3282\n",
      "3000 / 3282\n",
      "3200 / 3282\n",
      "CPU times: user 27.1 s, sys: 2.16 s, total: 29.2 s\n",
      "Wall time: 30.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "word_to_most_common_protein = retrieve_most_common_protein_from_words(\n",
    "    assembly_accessions, \n",
    "    most_common_words,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1694,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DEAD/DEAH box helicase'"
      ]
     },
     "execution_count": 1694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_to_most_common_protein['helicase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1700,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_most_common_word_output(\n",
    "    most_common_words, \n",
    "    word_to_most_common_protein,\n",
    "    top_term_freq, \n",
    "    all_term_freq,\n",
    "    top_document_freq,\n",
    "    all_document_freq,\n",
    "):\n",
    "    columns = [\n",
    "        'Keyword',\n",
    "        'Most common protein',\n",
    "        'Frequency',\n",
    "        'Frequency in top',\n",
    "        'Presence count',\n",
    "        'Present count in top',\n",
    "        'Term frequency score',\n",
    "        'Presence score',\n",
    "        'Score',\n",
    "    ]\n",
    "    data = []\n",
    "    for word in most_common_words:\n",
    "        term_score = top_term_freq[word] / all_term_freq[word]\n",
    "        presence_score = top_document_freq[word] / all_document_freq[word]\n",
    "        score = (term_score + presence_score) / 2\n",
    "        data.append([\n",
    "            word,\n",
    "            word_to_most_common_protein[word],\n",
    "            all_term_freq[word],\n",
    "            top_term_freq[word],\n",
    "            all_document_freq[word],\n",
    "            top_document_freq[word],\n",
    "            np.round(term_score, 2),\n",
    "            np.round(presence_score, 2),\n",
    "            np.round(score, 2),\n",
    "        ])\n",
    "    \n",
    "    return pd.DataFrame(data, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1704,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Keyword</th>\n",
       "      <th>Most common protein</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Frequency in top</th>\n",
       "      <th>Presence count</th>\n",
       "      <th>Present count in top</th>\n",
       "      <th>Term frequency score</th>\n",
       "      <th>Presence score</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>helicase-exonuclease</td>\n",
       "      <td>helicase-exonuclease AddAB subunit AddA</td>\n",
       "      <td>506</td>\n",
       "      <td>445</td>\n",
       "      <td>303</td>\n",
       "      <td>278</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>addab</td>\n",
       "      <td>helicase-exonuclease AddAB subunit AddA</td>\n",
       "      <td>519</td>\n",
       "      <td>448</td>\n",
       "      <td>309</td>\n",
       "      <td>282</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adda</td>\n",
       "      <td>helicase-exonuclease AddAB subunit AddA</td>\n",
       "      <td>466</td>\n",
       "      <td>390</td>\n",
       "      <td>465</td>\n",
       "      <td>390</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dnae</td>\n",
       "      <td>DNA polymerase III DnaE</td>\n",
       "      <td>130</td>\n",
       "      <td>104</td>\n",
       "      <td>122</td>\n",
       "      <td>100</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>addb</td>\n",
       "      <td>helicase-exonuclease AddAB subunit AddB</td>\n",
       "      <td>394</td>\n",
       "      <td>315</td>\n",
       "      <td>394</td>\n",
       "      <td>315</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spra</td>\n",
       "      <td>cell surface protein SprA</td>\n",
       "      <td>169</td>\n",
       "      <td>131</td>\n",
       "      <td>166</td>\n",
       "      <td>131</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>modules</td>\n",
       "      <td>Erythronolide synthase, modules 3 and 4</td>\n",
       "      <td>175</td>\n",
       "      <td>112</td>\n",
       "      <td>51</td>\n",
       "      <td>43</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>polc</td>\n",
       "      <td>DNA polymerase III PolC</td>\n",
       "      <td>262</td>\n",
       "      <td>186</td>\n",
       "      <td>233</td>\n",
       "      <td>180</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>non-ribosomal</td>\n",
       "      <td>non-ribosomal peptide synthetase</td>\n",
       "      <td>2267</td>\n",
       "      <td>1569</td>\n",
       "      <td>543</td>\n",
       "      <td>426</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>adenylation</td>\n",
       "      <td>amino acid adenylation domain-containing protein</td>\n",
       "      <td>1889</td>\n",
       "      <td>1285</td>\n",
       "      <td>513</td>\n",
       "      <td>396</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>duf490</td>\n",
       "      <td>protein of unknown function DUF490</td>\n",
       "      <td>317</td>\n",
       "      <td>218</td>\n",
       "      <td>288</td>\n",
       "      <td>205</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>alpha-2-macroglobulin</td>\n",
       "      <td>alpha-2-macroglobulin</td>\n",
       "      <td>479</td>\n",
       "      <td>321</td>\n",
       "      <td>423</td>\n",
       "      <td>295</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cobn</td>\n",
       "      <td>cobaltochelatase subunit CobN</td>\n",
       "      <td>784</td>\n",
       "      <td>515</td>\n",
       "      <td>659</td>\n",
       "      <td>465</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lhr</td>\n",
       "      <td>ATP dependent helicase, Lhr family</td>\n",
       "      <td>210</td>\n",
       "      <td>137</td>\n",
       "      <td>172</td>\n",
       "      <td>119</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>transcription-repair</td>\n",
       "      <td>transcription-repair coupling factor</td>\n",
       "      <td>2499</td>\n",
       "      <td>1629</td>\n",
       "      <td>2454</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>transcription-repair-coupling</td>\n",
       "      <td>Transcription-repair-coupling factor</td>\n",
       "      <td>372</td>\n",
       "      <td>236</td>\n",
       "      <td>334</td>\n",
       "      <td>219</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ca2+-transporting</td>\n",
       "      <td>Ca2+-transporting ATPase</td>\n",
       "      <td>216</td>\n",
       "      <td>135</td>\n",
       "      <td>148</td>\n",
       "      <td>97</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>polc-type</td>\n",
       "      <td>PolC-type DNA polymerase III</td>\n",
       "      <td>613</td>\n",
       "      <td>358</td>\n",
       "      <td>518</td>\n",
       "      <td>356</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>hrpa</td>\n",
       "      <td>ATP-dependent RNA helicase HrpA</td>\n",
       "      <td>915</td>\n",
       "      <td>557</td>\n",
       "      <td>840</td>\n",
       "      <td>554</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>alpha-mannosidase</td>\n",
       "      <td>alpha-mannosidase</td>\n",
       "      <td>1355</td>\n",
       "      <td>743</td>\n",
       "      <td>662</td>\n",
       "      <td>463</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Keyword  \\\n",
       "0            helicase-exonuclease   \n",
       "1                           addab   \n",
       "2                            adda   \n",
       "3                            dnae   \n",
       "4                            addb   \n",
       "5                            spra   \n",
       "6                         modules   \n",
       "7                            polc   \n",
       "8                   non-ribosomal   \n",
       "9                     adenylation   \n",
       "10                         duf490   \n",
       "11          alpha-2-macroglobulin   \n",
       "12                           cobn   \n",
       "13                            lhr   \n",
       "14           transcription-repair   \n",
       "15  transcription-repair-coupling   \n",
       "16              ca2+-transporting   \n",
       "17                      polc-type   \n",
       "18                           hrpa   \n",
       "19              alpha-mannosidase   \n",
       "\n",
       "                                 Most common protein  Frequency  \\\n",
       "0            helicase-exonuclease AddAB subunit AddA        506   \n",
       "1            helicase-exonuclease AddAB subunit AddA        519   \n",
       "2            helicase-exonuclease AddAB subunit AddA        466   \n",
       "3                            DNA polymerase III DnaE        130   \n",
       "4            helicase-exonuclease AddAB subunit AddB        394   \n",
       "5                          cell surface protein SprA        169   \n",
       "6            Erythronolide synthase, modules 3 and 4        175   \n",
       "7                            DNA polymerase III PolC        262   \n",
       "8                   non-ribosomal peptide synthetase       2267   \n",
       "9   amino acid adenylation domain-containing protein       1889   \n",
       "10                protein of unknown function DUF490        317   \n",
       "11                             alpha-2-macroglobulin        479   \n",
       "12                     cobaltochelatase subunit CobN        784   \n",
       "13                ATP dependent helicase, Lhr family        210   \n",
       "14              transcription-repair coupling factor       2499   \n",
       "15              Transcription-repair-coupling factor        372   \n",
       "16                          Ca2+-transporting ATPase        216   \n",
       "17                      PolC-type DNA polymerase III        613   \n",
       "18                   ATP-dependent RNA helicase HrpA        915   \n",
       "19                                 alpha-mannosidase       1355   \n",
       "\n",
       "    Frequency in top  Presence count  Present count in top  \\\n",
       "0                445             303                   278   \n",
       "1                448             309                   282   \n",
       "2                390             465                   390   \n",
       "3                104             122                   100   \n",
       "4                315             394                   315   \n",
       "5                131             166                   131   \n",
       "6                112              51                    43   \n",
       "7                186             233                   180   \n",
       "8               1569             543                   426   \n",
       "9               1285             513                   396   \n",
       "10               218             288                   205   \n",
       "11               321             423                   295   \n",
       "12               515             659                   465   \n",
       "13               137             172                   119   \n",
       "14              1629            2454                  1626   \n",
       "15               236             334                   219   \n",
       "16               135             148                    97   \n",
       "17               358             518                   356   \n",
       "18               557             840                   554   \n",
       "19               743             662                   463   \n",
       "\n",
       "    Term frequency score  Presence score  Score  \n",
       "0                   0.88            0.92   0.90  \n",
       "1                   0.86            0.91   0.89  \n",
       "2                   0.84            0.84   0.84  \n",
       "3                   0.80            0.82   0.81  \n",
       "4                   0.80            0.80   0.80  \n",
       "5                   0.78            0.79   0.78  \n",
       "6                   0.64            0.84   0.74  \n",
       "7                   0.71            0.77   0.74  \n",
       "8                   0.69            0.78   0.74  \n",
       "9                   0.68            0.77   0.73  \n",
       "10                  0.69            0.71   0.70  \n",
       "11                  0.67            0.70   0.68  \n",
       "12                  0.66            0.71   0.68  \n",
       "13                  0.65            0.69   0.67  \n",
       "14                  0.65            0.66   0.66  \n",
       "15                  0.63            0.66   0.65  \n",
       "16                  0.62            0.66   0.64  \n",
       "17                  0.58            0.69   0.64  \n",
       "18                  0.61            0.66   0.63  \n",
       "19                  0.55            0.70   0.62  "
      ]
     },
     "execution_count": 1704,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_word_output = compute_most_common_word_output(\n",
    "    most_common_words, \n",
    "    word_to_most_common_protein,\n",
    "    top_term_freq, \n",
    "    all_term_freq,\n",
    "    top_document_freq,\n",
    "    all_document_freq,\n",
    ")\n",
    "most_common_word_output.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1707,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_common_word_output.to_excel(\n",
    "    os.path.join(os.getcwd(), 'data/codon_bias_common_proteins.xlsx'), \n",
    "    index=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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